diff --git a/mindinsight/backend/datavisual/train_visual_api.py b/mindinsight/backend/datavisual/train_visual_api.py
index 72bb45eabc0d2db9506a2761af077a9609de8fe3..24fcd6ce4da21c320e73898d33ec5b89f2c1d7ca 100644
--- a/mindinsight/backend/datavisual/train_visual_api.py
+++ b/mindinsight/backend/datavisual/train_visual_api.py
@@ -102,12 +102,11 @@ def graph_nodes():
 
     """
     name = request.args.get('name', default=None)
-    node_type = request.args.get('type', default='name_scope')
     tag = request.args.get("tag", default=None)
     train_id = get_train_id(request)
 
     graph_process = GraphProcessor(train_id, DATA_MANAGER, tag)
-    response = graph_process.get_nodes(name=name, node_type=node_type)
+    response = graph_process.list_nodes(scope=name)
     return jsonify(response)
 
 
diff --git a/mindinsight/datavisual/data_transform/graph/graph.py b/mindinsight/datavisual/data_transform/graph/graph.py
index 24ba5d56579129da8e9b118887858bfd21eb00d0..0744cace69ed059899c2818dacb56f92633a7e8f 100644
--- a/mindinsight/datavisual/data_transform/graph/graph.py
+++ b/mindinsight/datavisual/data_transform/graph/graph.py
@@ -15,13 +15,13 @@
 """
 This file is used to define the basic graph.
 """
-import copy
-import time
-
 from enum import Enum
+from collections import defaultdict
 
-from mindinsight.datavisual.common.log import logger
 from mindinsight.datavisual.common.exceptions import NodeNotInGraphError
+from mindinsight.datavisual.common.log import logger
+from mindinsight.utils.exceptions import ParamMissError
+from mindinsight.utils.exceptions import ParamValueError
 from .node import NodeTypeEnum
 from .node import Node
 
@@ -32,27 +32,38 @@ class EdgeTypeEnum(Enum):
     DATA = 'data'
 
 
-class DataTypeEnum(Enum):
-    """Data type enum."""
-    DT_TENSOR = 13
-
-
 class Graph:
     """The `Graph` object is used to describe a graph file."""
-    MIN_POLYMERIC_NODE_COUNT = 5
+    # Limit the size of a single attribute value per node to avoid storing too much data
+    MAX_NODE_ATTRIBUTE_VALUE_BYTES = 1024
 
-    def __init__(self):
-        # Store nodes contain leaf nodes, name scope node, except polymeric nodes
-        self._normal_nodes = {}
+    # In the same scope, the number of children of the same type exceeds this threshold, and we will combine them.
+    MIN_GROUP_NODE_COUNT = 5
 
-        # Store polymeric nodes.
-        self._polymeric_nodes = {}
-
-        # Store all nodes resolved from the file.
-        self._leaf_nodes = {}
-
-        # The format of node groups is {'group_name': {'node_name': <Node>}}
-        self._node_groups = {}
+    def __init__(self):
+        # Used to cache all nodes, and the key is node name, value is `Node` object.
+        self._normal_node_map = {}
+        self._node_id_map_name = {}
+
+        # The additional caching of Const and Parameter is to handle the Const
+        # and Parameter nodes separately later.
+        self._const_node_temp_cache = {}
+        self._parameter_node_temp_cache = {}
+
+    def build_graph(self, proto_data):
+        """This method is used to build the graph."""
+
+        # Notice:
+        # The following methods are interdependent and cannot be switched at will.
+        self._parse_data(proto_data)
+        self._add_variable_nodes(NodeTypeEnum.PARAMETER.value)
+        self._build_aggregation_scope_nodes()
+        self._process_independent_layout()
+        self._build_name_scope_nodes()
+
+        # Since const nodes are not aggregated, adding them at the end can save a lot of computation.
+        self._add_variable_nodes(NodeTypeEnum.CONST.value)
+        self._calc_subnode_count()
 
     def exist_node(self, name):
         """
@@ -62,52 +73,27 @@ class Graph:
             name (str): The node name.
 
         Returns:
-            bool, if node is exist will return True.
+            bool, if node exists, will return True.
 
         """
-        if self._normal_nodes.get(name) is None:
+        if name is None:
             return False
-        return True
+        return self._is_node_exist(node_name=name)
 
-    def get_normal_nodes(self, namescope=None):
+    def list_node_by_scope(self, scope=None):
         """
-        Get nodes by namescope.
+        List nodes by the scope of nodes. The scope of a node is the same as its parent node name.
 
         Args:
-            namescope (str): A namescope of nodes.
+            scope (str): A scope of nodes.
 
         Returns:
             list[dict], a list object contain `Node` object.
-
         """
+        scope = "" if scope is None else scope
         nodes = []
-        if namescope is None:
-            for name, node in self._normal_nodes.items():
-                if '/' not in name:
-                    # Get first layer nodes
-                    nodes.append(node.to_dict())
-            return nodes
-
-        namescope = namescope + '/'
-        for name, node in self._normal_nodes.items():
-            if name.startswith(namescope) and '/' not in name.split(namescope)[1]:
-                nodes.append(node.to_dict())
-
-        return nodes
-
-    def get_polymeric_nodes(self, polymeric_scope):
-        """
-        Get polymeric nodes by polymeric scope.
-
-        Args:
-            polymeric_scope (str): The polymeric scope name of nodes.
-
-        Returns:
-            list[dict], a list object contain `Node` object.
-        """
-        nodes = []
-        for node in self._polymeric_nodes.values():
-            if node.polymeric_scope_name == polymeric_scope:
+        for node in self._normal_node_map.values():
+            if node.scope == scope:
                 nodes.append(node.to_dict())
         return nodes
 
@@ -117,21 +103,18 @@ class Graph:
 
         Args:
             content (Union[str, None]): This content can be the key content of the node to search,
-                if None, will get all node names.
+                                        if None, will get all node names.
             offset (int): An offset for page. Ex, offset is 0, mean current page is 1.
             limit (int): An offset for page. Ex, offset is 0, mean current page is 1.
 
         Returns:
             list[str], a list of node names.
         """
-        all_names = []
-        all_names.extend(list(self._normal_nodes.keys()))
-        all_names.extend(list(self._polymeric_nodes.keys()))
         if content is not None:
             content = content.lower()
-            catch_names = [name for name in all_names if content in name.lower()]
+            catch_names = [name for name in self._normal_node_map if content in name.lower()]
         else:
-            catch_names = all_names
+            catch_names = list(self._normal_node_map)
         catch_names = sorted(catch_names)
         real_offset = offset * limit
         return catch_names[real_offset:real_offset+limit]
@@ -149,304 +132,419 @@ class Graph:
                                'scope_name': '<Node scope>',
                                'children': {<item_object>}}
         """
-        if node_name and self._polymeric_nodes.get(node_name) is None \
-                and self._normal_nodes.get(node_name) is None:
+        if node_name and not self.exist_node(name=node_name):
             raise NodeNotInGraphError(node_name=node_name)
 
         response = {}
-        nodes = self.get_normal_nodes()
+        nodes = self.list_node_by_scope()
         response.update({
             'nodes': nodes,
             'scope_name': '',
             'children': {}
         })
 
-        names = node_name.split('/')
         children = response['children']
-        for i in range(1, len(names)+1):
-            if i == len(names):
-                polymeric_node = self._polymeric_nodes.get(node_name)
-                if polymeric_node:
-                    polymeric_scope = polymeric_node.polymeric_scope_name
-                    nodes = self.get_polymeric_nodes(polymeric_scope)
-                    children.update({'nodes': nodes,
-                                     'scope_name': polymeric_scope,
-                                     'children': {}})
-                break
-
-            name_scope = '/'.join(names[:i])
-            nodes = self.get_normal_nodes(name_scope)
+
+        index = node_name.find('/')
+        while index != -1:
+            scope = node_name[:index]
+            nodes = self.list_node_by_scope(scope)
             children.update({
                 'nodes': nodes,
-                'scope_name': name_scope,
+                'scope_name': scope,
                 'children': {}
             })
             children = children['children']
 
+            index = node_name.find('/', index+1)
+
         return response
 
-    def _build_polymeric_nodes(self):
-        """Build polymeric node."""
-        logger.debug("Start to build polymeric nodes")
-
-        self._find_polymeric_nodes()
-
-        group_count_map = {}
-        for group_name, group in self._node_groups.items():
-            name = group_name.split('/')[-1]
-            count = group_count_map.get(name, 0)
-            count += 1
-            group_count_map[name] = count
-            polymeric_node_name = group_name + '_{}_[{}]'.format(count, len(group))
-            polymeric_node = Node(polymeric_node_name, node_id=polymeric_node_name)
-            polymeric_node.node_type = NodeTypeEnum.POLYMERIC_SCOPE.value
-            polymeric_node.name_scope = '/'.join(group_name.split('/')[:-1])
-            polymeric_node.subnode_count = len(group)
-
-            for name_tmp, node_tmp in group.items():
-                node_tmp.polymeric_scope_name = polymeric_node_name
-                self._polymeric_nodes.update({name_tmp: node_tmp})
-                polymeric_node.update_input(node_tmp.inputs)
-                polymeric_node.update_output(node_tmp.outputs)
-
-            self._normal_nodes.update({polymeric_node_name: polymeric_node})
-
-        self._update_input_output()
-
-    def _find_polymeric_nodes(self):
-        """Find polymeric nodes from node groups."""
-        node_groups = copy.deepcopy(self._node_groups)
-        for group_name, group in node_groups.items():
-            if len(group) < self.MIN_POLYMERIC_NODE_COUNT:
-                self._normal_nodes.update(group)
-                self._node_groups.pop(group_name)
-                continue
+    def _parse_data(self, proto_data):
+        """
+        This method will parse the data and create basic nodes to store in the cache.
 
-            move_node_names = []
-            is_move_group = False
-            for node_name, group_node in group.items():
-                node_list = []
-                is_in_group = False
-                for dst_name in group_node.outputs:
-                    node_tmp = self._leaf_nodes[dst_name]
-                    node_list.append(node_tmp)
-
-                start = time.time()
-                run_count = 0
-                visit_nodes = {}
-                while node_list:
-                    # Iterate to find if the output of the node in the group causes a loop
-                    # example: there is a group A, and node_a is a Node in group.
-                    # if there is a loop in node_a, like A/node_a -> B/node_b -> A/node_b
-                    # we will remove the node_a from group A.
-                    node_tmp = node_list[0]
-                    node_list = node_list[1:]
-                    visit_nodes.update({node_tmp.name: True})
-                    if node_tmp in group.values():
-                        is_in_group = True
-                        break
-                    for dst_name_tmp in node_tmp.outputs:
-                        run_count += 1
-                        node_tmp = self._leaf_nodes[dst_name_tmp]
-                        if visit_nodes.get(dst_name_tmp):
+        The graph is then built based on the cache.
+        """
+        raise NotImplementedError("Before you can build a graph, you need to parse the data.")
+
+    def _build_name_scope_nodes(self):
+        """
+        Build name scope node by every node name.
+
+        We create the name scope node by the slash('/') in the node name.
+        For example, if a node name is "Default/add", we generate a scope named 'Default' based on slash('/') and
+        create a name scope node named 'Default'.
+        """
+        logger.info("Start to build name scope nodes.")
+        scope_node_map = {}
+        for name, node in self._normal_node_map.items():
+            index = name.find('/')
+            pre_index = None
+            while index > 0:
+                scope = name[:index]
+                scope_node = scope_node_map.get(scope)
+                if scope_node is None:
+                    if self._is_node_exist(node_name=scope):
+                        exist_node = self._get_normal_node(node_name=scope)
+                        if exist_node.type == NodeTypeEnum.AGGREGATION_SCOPE.value:
+                            # This scope is aggregation scope, so we don't have to do anything.
+                            pre_index = index
+                            index = name.find('/', pre_index + 1)
                             continue
-                        node_list.append(node_tmp)
-                logger.debug("Find group %s node end, is_in_group: %s, use time: %s, "
-                             "run count: %s.", group_name, is_in_group,
-                             time.time() - start, run_count)
 
-                if is_in_group:
-                    move_node_names.append(node_name)
+                        # We find a node name that conflicts with the current scope and rename the node
+                        self._update_conflict_node(conflict_name=scope)
 
-                if (len(group) - len(move_node_names)) < self.MIN_POLYMERIC_NODE_COUNT:
-                    is_move_group = True
-                    break
+                    # We create a node for current scope.
+                    scope_node = Node(scope, node_id=scope)
+                    scope_node.type = NodeTypeEnum.NAME_SCOPE.value
+                    scope_node.scope = '' if pre_index is None else name[:pre_index]
+                    scope_node_map.update({scope_node.name: scope_node})
 
-            if is_move_group:
-                self._normal_nodes.update(group)
-                self._node_groups.pop(group_name)
-            else:
-                for name_tmp in move_node_names:
-                    node_tmp = self._node_groups[group_name].pop(name_tmp)
-                    self._normal_nodes.update({name_tmp: node_tmp})
-
-    def _update_input_output(self):
-        """We need to update input and output attribute after build polymeric node."""
-        for node in self._normal_nodes.values():
-            for src_name, input_attr in node.inputs.items():
-                if self._polymeric_nodes.get(src_name):
-                    input_attr['scope'] = NodeTypeEnum.POLYMERIC_SCOPE.value
-                    node.update_input({src_name: input_attr})
-
-            for dst_name, output_attr in node.outputs.items():
-                if self._polymeric_nodes.get(dst_name):
-                    output_attr['scope'] = NodeTypeEnum.POLYMERIC_SCOPE.value
-                    node.update_output({dst_name: output_attr})
-
-        for node in self._polymeric_nodes.values():
-            for src_name, input_attr in node.inputs.items():
-                if self._polymeric_nodes.get(src_name):
-                    input_attr['scope'] = NodeTypeEnum.POLYMERIC_SCOPE.value
-                    node.update_input({src_name: input_attr})
-
-            for dst_name, output_attr in node.outputs.items():
-                if self._polymeric_nodes.get(dst_name):
-                    output_attr['scope'] = NodeTypeEnum.POLYMERIC_SCOPE.value
-                    node.update_output({dst_name: output_attr})
-
-    def _update_polymeric_input_output(self):
-        """Calc polymeric input and output after build polymeric node."""
-        for node in self._normal_nodes.values():
-            polymeric_input = self._calc_polymeric_attr(node, 'inputs')
-            node.update_polymeric_input(polymeric_input)
-
-            polymeric_output = self._calc_polymeric_attr(node, 'outputs')
-            node.update_polymeric_output(polymeric_output)
-
-        for name, node in self._polymeric_nodes.items():
-            polymeric_input = {}
-            for src_name in node.inputs:
-                output_name = self._calc_dummy_node_name(name, src_name)
-                polymeric_input.update({output_name: {'edge_type': EdgeTypeEnum.DATA.value}})
-            node.update_polymeric_input(polymeric_input)
-
-            polymeric_output = {}
-            for dst_name in node.outputs:
-                polymeric_output = {}
-                output_name = self._calc_dummy_node_name(name, dst_name)
-                polymeric_output.update({output_name: {'edge_type': EdgeTypeEnum.DATA.value}})
-            node.update_polymeric_output(polymeric_output)
-
-    def _calc_polymeric_attr(self, node, attr):
+                # Inherit input and output from sub nodes.
+                self._inherit_input_output_from_subnode(scope_node, subnode_list=[node])
+
+                pre_index = index
+                index = name.find('/', pre_index+1)
+
+        # Cache all the scope node to normal node dict
+        for node in scope_node_map.values():
+            self._cache_node(node)
+
+    def _update_conflict_node(self, conflict_name):
+        conflict_node = self._get_normal_node(node_name=conflict_name)
+        base_name = conflict_name.split('/')[-1]
+        new_name = Node.create_node_name(scope=conflict_node.scope, base_name=base_name)
+        self._update_node_name_of_cache(conflict_node, new_name, update_parent=True)
+
+    def _inherit_input_output_from_subnode(self, parent_node, subnode_list, filtered_type=None):
         """
-        Calc polymeric input or polymeric output after build polymeric node.
+        Adds the input and output of all direct child nodes to the current node.
 
         Args:
-            node (Node): Computes the polymeric input for a given node.
-            attr (str): The polymeric attr, optional value is `input` or `output`.
+            parent_node (Node): The nodes that inherit the input and output of the child nodes.
+            subnode_list (list[Node]): A list of child nodes that are inherited from the input and output.
+            filtered_type (set(str)): Filter some input and output that do not require inheritance
+                                      based on the node type. Default is filter const node.
+
+        Note:
+            - Only the inputs and outputs of the external scope are inherited.
+            - Before add_const_node method, if the input is a const,
+              the scope of the const node is not startswith the name of parent node.
+              So in this scenario, we need to filter the const nodes.
+        """
+        filtered_type = {NodeTypeEnum.CONST.value} if filtered_type is None else filtered_type
+        for method in ['input', 'output', 'proxy_input', 'proxy_output']:
+            for node in subnode_list:
+                for item_name, item_attr in getattr(node, method).items():
+                    target_node = self._get_normal_node(node_name=item_name)
+                    if item_name.startswith(f'{parent_node.name}/'):
+                        # Own scope, ignore
+                        continue
 
-        Returns:
-            dict, return polymeric input or polymeric output of the given node.
+                    if target_node.type in filtered_type:
+                        continue
+
+                    getattr(parent_node, f'add_{method}')(item_name, item_attr)
+
+    def _build_aggregation_scope_nodes(self):
         """
-        polymeric_attr = {}
-        for node_name in getattr(node, attr):
-            polymeric_node = self._polymeric_nodes.get(node_name)
-            if node.node_type == NodeTypeEnum.POLYMERIC_SCOPE.value:
-                node_name = node_name if not polymeric_node else polymeric_node.polymeric_scope_name
-                dummy_node_name = self._calc_dummy_node_name(node.name, node_name)
-                polymeric_attr.update({dummy_node_name: {'edge_type': EdgeTypeEnum.DATA.value}})
-                continue
+        Under the same scope, the number of nodes of the same type will be aggregated after exceeding the set threshold.
 
-            if not polymeric_node:
-                continue
+        Note:
+            The threshold value refers to the `MIN_GROUP_NODE_COUNT`.
+        """
+        logger.info("Start to build aggregation scope nodes.")
+        group_node_map, filtered_group_names = self._find_group_nodes()
+
+        # create merge scope nodes
+        aggregation_scope_node_map = {}
+        for i, group_name in enumerate(filtered_group_names):
+            slash_index = group_name.rfind('/')
+            if slash_index != -1:
+                scope, op_type = group_name[:slash_index], group_name[slash_index+1:]
+            else:
+                scope, op_type = '', group_name
+
+            count = len(group_node_map.get(group_name))
+            aggregation_node_name = Node.create_node_name(scope=scope, base_name=f'{op_type}[{count}]_{i}')
+            aggregation_scope_node = Node(name=aggregation_node_name, node_id=aggregation_node_name)
+            aggregation_scope_node.subnode_count = count
+            aggregation_scope_node.scope = scope
+            aggregation_scope_node.type = NodeTypeEnum.AGGREGATION_SCOPE.value
+
+            # Update the name and scope of all children nodes
+            for node in group_node_map[group_name]:
+                base_name = node.name.split('/')[-1]
+                new_name = Node.create_node_name(scope=aggregation_node_name, base_name=base_name)
+                node.scope = aggregation_node_name
+
+                # Since the name scope has not been created, there is no need to update the parent node.
+                self._update_node_name_of_cache(node, new_name, update_parent=False)
+
+            # Cache this node
+            self._cache_node(aggregation_scope_node)
+            aggregation_scope_node_map.update({group_name: aggregation_scope_node})
+
+        # Adds the input and output of all direct child nodes to the current node.
+        for group_name, node in aggregation_scope_node_map.items():
+            self._inherit_input_output_from_subnode(node, group_node_map[group_name])
+
+    def _find_group_nodes(self):
+        """
+        Find nodes that can be grouped into a group.
 
-            if not node.name_scope and polymeric_node.name_scope:
-                # If current node is in top-level layer, and the polymeric_node node is not in
-                # the top-level layer, the polymeric node will not be the polymeric input
-                # or polymeric output of current node.
+        For direct child nodes in a scope, we divide them into multiple groups by node type.
+        However, we will exclude several types of child nodes,
+        because these types of nodes are not operational nodes.
+        """
+        exclude_types = {
+            NodeTypeEnum.CONST.value,
+            NodeTypeEnum.NAME_SCOPE.value,
+        }
+
+        group_node_map = defaultdict(list)
+        for node in self._normal_node_map.values():
+            if node.type in exclude_types:
                 continue
+            group_name = Node.create_node_name(scope=node.scope, base_name=node.type)
+            group_node_map[group_name].append(node)
 
-            if node.name_scope == polymeric_node.name_scope \
-                    or node.name_scope.startswith(polymeric_node.name_scope + '/'):
-                polymeric_attr.update(
-                    {polymeric_node.polymeric_scope_name: {'edge_type': EdgeTypeEnum.DATA.value}})
+        # filter can group scope.
+        filtered_group_names = []
+        for name, nodes in group_node_map.items():
+            if len(nodes) < self.MIN_GROUP_NODE_COUNT:
+                continue
+            filtered_group_names.append(name)
 
-        return polymeric_attr
+        return group_node_map, filtered_group_names
 
-    def _calc_dummy_node_name(self, current_node_name, other_node_name):
+    def _add_variable_nodes(self, node_type):
         """
-        Calc dummy node name.
+        We create the Const nodes or Parameter nodes in this method.
 
         Args:
-            current_node_name (str): The name of current node.
-            other_node_name (str): The target dummy node name.
+            node_type (str): Decide which type of node to add.
+                             Optional is `NodeTypeEnum.CONST.value` and `NodeTypeEnum.PARAMETER.value`.
 
-        Returns:
-            str, the dummy node name.
+        Note:
+            This method relies on the presence of data in the const cache or parameter cache.
         """
-        name_tmp = other_node_name
-        if self._polymeric_nodes.get(other_node_name):
-            name_tmp = self._polymeric_nodes[other_node_name].polymeric_scope_name
-        name_tmp_list = name_tmp.split('/')
-        current_name_list = current_node_name.split('/')
-        index = 0
-        min_len = min(len(name_tmp_list), len(current_name_list))
-        for i in range(min_len):
-            index = i
-            if name_tmp_list[index] != current_name_list[index]:
-                break
-        dummy_node_name = '/'.join(name_tmp_list[:index+1])
-        return dummy_node_name
+        logger.info("Start to add %s nodes to each scope in graph.", node_type)
+        node_map = {}
+        for node in self._normal_node_map.values():
+            for src_name, input_attr in dict(node.input).items():
+
+                if node_type == NodeTypeEnum.CONST.value and not self._const_node_temp_cache.get(src_name):
+                    continue
+
+                if node_type == NodeTypeEnum.PARAMETER.value and not self._parameter_node_temp_cache.get(src_name):
+                    continue
+
+                variable_name = Node.create_node_name(scope=node.scope, base_name=src_name)
+                if node_map.get(variable_name):
+                    # There is no need to create the node repeatedly
+                    variable_node = node_map.get(variable_name)
+                else:
+                    cache_node = self._get_normal_node(node_name=src_name)
+                    variable_node = Node(name=variable_name, node_id=variable_name)
+                    Node.copy_node_without_input_output(cache_node, variable_node)
+                    variable_node.scope = node.scope
+
+                variable_node.add_output(dst_name=node.name, output_attr=input_attr)
+                node_map.update({variable_name: variable_node})
+
+                node.delete_input(src_name)
+                node.add_input(variable_name, input_attr)
+
+        for node in node_map.values():
+            self._cache_node(node)
+
+        # Remove nodes that are not used in the cache.
+        if node_type == NodeTypeEnum.CONST.value:
+            unused_names = set(self._const_node_temp_cache) - set(node_map)
+        elif node_type == NodeTypeEnum.PARAMETER.value:
+            unused_names = set(self._parameter_node_temp_cache) - set(node_map)
+        else:
+            raise ParamValueError("The node type should be const or parameter.")
 
-    def _build_name_scope_nodes(self):
-        """Build name scope node by every node name."""
-        normal_nodes = dict(self._normal_nodes)
-
-        rename_node_names = {}
-        for name, node in normal_nodes.items():
-            name_list = name.split('/')
-            for i in range(1, len(name_list)):
-                name_scope = '/'.join(name_list[:i])
-                name_scope_node = self._normal_nodes.get(name_scope)
-                if name_scope_node is None:
-                    name_scope_node = Node(name_scope, node_id=name_scope)
-                    name_scope_node.node_type = NodeTypeEnum.NAME_SCOPE.value
-                    name_scope_node.name_scope = '/'.join(name_list[:i-1])
-                elif name_scope_node.node_type != NodeTypeEnum.NAME_SCOPE.value:
-                    # The name of this node conflicts with namescope, so rename this node
-                    old_name = name_scope_node.name
-                    old_names = name_scope_node.name.split('/')
-                    old_names[-1] = f'({old_names[-1]})'
-                    new_name = '/'.join(old_names)
-                    name_scope_node.name = new_name
-                    self._normal_nodes.pop(old_name)
-                    self._normal_nodes.update({new_name: name_scope_node})
-                    rename_node_names.update({old_name: new_name})
-
-                    # create new namescope
-                    name_scope_node = Node(name_scope, node_id=name_scope)
-                    name_scope_node.node_type = NodeTypeEnum.NAME_SCOPE.value
-                    name_scope_node.name_scope = '/'.join(name_list[:i-1])
-
-                # update the input and output of this to namescope node
-                name_scope_with_slash = name_scope + '/'
-                for src_name, input_attr in node.inputs.items():
-                    if src_name.startswith(name_scope_with_slash):
-                        continue
-                    name_scope_node.update_input({src_name: input_attr})
+        self._delete_nodes_of_cache(unused_names)
 
-                for dst_name, output_attr in node.outputs.items():
-                    if dst_name.startswith(name_scope_with_slash):
-                        continue
-                    name_scope_node.update_output({dst_name: output_attr})
-
-                self._normal_nodes.update({name_scope: name_scope_node})
-
-        if rename_node_names:
-            # If existing nodes are renamed, the inputs and outputs of all nodes need to be refreshed
-            nodes = []
-            nodes.extend(self._normal_nodes.values())
-            nodes.extend(self._polymeric_nodes.values())
-            for node in nodes:
-                attrs = ['inputs', 'outputs', 'polymeric_inputs', 'polymeric_outputs']
-                for item in attrs:
-                    tmp_dict = dict(getattr(node, item))
-                    for name, value in tmp_dict.items():
-                        new_name = rename_node_names.get(name, False)
-                        if new_name:
-                            getattr(node, item).pop(name)
-                            getattr(node, f'update_{item}')({new_name: value})
+    def _calc_subnode_count(self):
+        """Calc all the direct sub node count."""
+        subnode_count_map = defaultdict(int)
+        for node in self._normal_node_map.values():
+            if not node.scope:
+                continue
 
-        self._calc_subnode_count()
+            if not self._is_node_exist(node_name=node.scope):
+                logger.warning("Can not find a scope node by the given name(%s), "
+                               "the name scope nodes may not have been created.", node.scope)
+                continue
+            subnode_count_map[node.scope] = subnode_count_map[node.scope] + 1
 
-    def _calc_subnode_count(self):
-        """Calc the sub node count of scope node."""
-        name_scope_mapping = {}
-        for node in self._normal_nodes.values():
-            if node.name_scope:
-                count = name_scope_mapping.get(node.name_scope, 0)
-                name_scope_mapping[node.name_scope] = count + 1
-
-        for name_scope, count in name_scope_mapping.items():
-            node = self._normal_nodes[name_scope]
+        for name, count in subnode_count_map.items():
+            node = self._get_normal_node(node_name=name)
             node.subnode_count = count
+
+    def _get_normal_node(self, node_id=None, node_name=None):
+        """Query node by node id or node name."""
+        if node_id is not None:
+            name = self._node_id_map_name.get(node_id)
+            node = self._normal_node_map.get(name)
+            return node
+
+        if node_name is not None:
+            return self._normal_node_map.get(node_name)
+
+        raise ParamMissError('Method requires an argument that is not None.')
+
+    def _is_node_exist(self, node_id=None, node_name=None):
+        """Check node is exist."""
+        if node_id is not None:
+            return bool(self._node_id_map_name.get(node_id))
+
+        if node_name is not None:
+            return bool(self._normal_node_map.get(node_name))
+
+        raise ParamMissError('Method requires an argument that is not None.')
+
+    @property
+    def normal_node_count(self):
+        """Get the normal node count."""
+        return len(self._normal_node_map)
+
+    def _cache_node(self, node):
+        """Store the node in the cache."""
+        # Notice:
+        # The additional caching of Const and Parameter is to handle the Const and Parameter nodes separately later.
+        if node.type == NodeTypeEnum.CONST.value:
+            self._const_node_temp_cache.update({node.name: node})
+        if node.type == NodeTypeEnum.PARAMETER.value:
+            self._parameter_node_temp_cache.update({node.name: node})
+
+        self._normal_node_map.update({node.name: node})
+        self._node_id_map_name.update({node.node_id: node.name})
+
+    def _delete_nodes_of_cache(self, node_names):
+        """Delete node from cache."""
+        logger.debug("These nodes will be removed from the cache, node names: %s.", str(node_names))
+        for name in node_names:
+
+            if self._parameter_node_temp_cache.get(name):
+                self._parameter_node_temp_cache.pop(name)
+            if self._const_node_temp_cache.get(name):
+                self._const_node_temp_cache.pop(name)
+
+            node = self._get_normal_node(node_name=name)
+            self._normal_node_map.pop(name)
+            self._node_id_map_name.pop(node.node_id)
+
+    def _update_node_name_of_cache(self, node, new_name, update_parent=False):
+        """
+        Update a node name which is stored in cache.
+
+        Args:
+            node (Node): The node that will be renamed.
+            new_name (str): The new name.
+            update_parent (bool): Determines whether the input and output of the parent node need to be updated.
+        """
+        logger.debug('Update node name of cache, node(%s), new name is %s.', str(node), new_name)
+        origin_name = node.name
+        node.name = new_name
+
+        # Find all nodes that need to modify the input and input
+        update_node_map = {}
+        for method in ['input', 'output', 'proxy_input', 'proxy_output']:
+            for target_name in getattr(node, method):
+                target_node = self._get_normal_node(node_name=target_name)
+                if target_node is None:
+                    message = f"Node should not be None, name: {target_name}, {method}: {list(getattr(node, method))}."
+                    logger.error(message)
+                    continue
+
+                update_node_map.update({target_name: target_node})
+
+                if not update_parent:
+                    continue
+
+                slash_index = target_name.find('/')
+                while slash_index != -1:
+                    scope_name = target_name[:slash_index]
+                    slash_index = target_name.find('/', slash_index+1)
+
+                    if update_node_map.get(scope_name):
+                        continue
+
+                    scope_node = self._get_normal_node(node_name=scope_name)
+                    update_node_map.update({scope_name: scope_node})
+
+        # Update the input and output of the nodes
+        for target_node in update_node_map.values():
+            for method in ['input', 'output', 'proxy_input', 'proxy_output']:
+                attr_temp = getattr(target_node, method).get(origin_name)
+                if attr_temp is None:
+                    # This method does not have this node, so it is skipped
+                    continue
+
+                # Delete the old attribute and update new name to source node or destination node.
+                getattr(target_node, f'delete_{method}')(origin_name)
+                getattr(target_node, f'add_{method}')(new_name, attr_temp)
+
+        # Delete the origin node in cache.
+        self._delete_nodes_of_cache(node_names=[origin_name])
+        self._cache_node(node)
+
+    def _process_independent_layout(self):
+        """Handle separate layout nodes."""
+        independent_layout_node_map = {}
+        for node in self._normal_node_map.values():
+            base_name = node.name.split('/')[-1]
+            if node.type == NodeTypeEnum.AGGREGATION_SCOPE.value and NodeTypeEnum.PARAMETER.value in base_name:
+                independent_layout_node_map[node.name] = node
+
+        # Find all sub nodes
+        subnode_map = defaultdict(list)
+        for node in self._normal_node_map.values():
+            if independent_layout_node_map.get(node.scope):
+                subnode_map[node.scope].append(node)
+
+        # Notice:
+        # The following processing is only done for the parameter node, other types of nodes are not processed.
+        # Later, when you need to extend to other nodes, the code needs to be adjusted.
+        for scope_node in independent_layout_node_map.values():
+            scope_node.independent_layout = True
+
+            method = 'output'
+            for target_name, target_attr in dict(getattr(scope_node, method)).items():
+                proxy_attr = dict(edge_type=target_attr['edge_type'])
+
+                target_node = self._get_normal_node(node_name=target_name)
+                getattr(target_node, 'add_proxy_input')(scope_node.name, proxy_attr)
+
+                # Note:
+                # If the source node and the destination node are not in the same scope,
+                # the proxy node is presented as scope in order to simplify the flow of the display data.
+                # For example, the data flow is parameter[5]_1 -> add[5]_1/add1
+                # we create a scope proxy node(add[5]_1) for parameter[5]_1,
+                # so there is a proxy data flow parameter[5]_1 -> add[5]_1 instead of parameter[5]_1 -> add[5]_1/add1.
+                if target_node.scope == scope_node.scope:
+                    getattr(scope_node, f'add_proxy_{method}')(target_name, proxy_attr)
+                else:
+                    target_scope_node = self._get_normal_node(node_name=target_node.scope)
+                    getattr(scope_node, f'add_proxy_{method}')(target_node.scope, proxy_attr)
+                    getattr(target_scope_node, 'add_proxy_input')(scope_node.name, proxy_attr)
+
+            for subnode in subnode_map[scope_node.name]:
+                for target_name, target_attr in dict(getattr(subnode, method)).items():
+                    proxy_attr = dict(edge_type=target_attr['edge_type'])
+                    target_node = self._get_normal_node(node_name=target_name)
+                    if target_node.scope == scope_node.scope:
+                        getattr(subnode, f'add_proxy_{method}')(target_name, proxy_attr)
+                    else:
+                        getattr(subnode, f'add_proxy_{method}')(target_node.scope, proxy_attr)
+
+                    input_attr = getattr(target_node, 'input')[subnode.name]
+                    input_attr['independent_layout'] = True
+                    target_node.add_input(subnode.name, input_attr)
diff --git a/mindinsight/datavisual/data_transform/graph/msgraph.py b/mindinsight/datavisual/data_transform/graph/msgraph.py
index 0f17084d246614a9b0848a2c1f0641d9a77ed805..66416bf4fe2f59d50b06f04476824db6e1b163fb 100644
--- a/mindinsight/datavisual/data_transform/graph/msgraph.py
+++ b/mindinsight/datavisual/data_transform/graph/msgraph.py
@@ -13,236 +13,135 @@
 # limitations under the License.
 # ============================================================================
 """This file is used to define the MindSpore graph."""
-import re
-import copy
+import time
 
 from mindinsight.datavisual.common.log import logger
+from mindinsight.datavisual.proto_files.mindinsight_anf_ir_pb2 import DataType
 from .node import Node
 from .node import NodeTypeEnum
 from .graph import Graph
 from .graph import EdgeTypeEnum
-from .graph import DataTypeEnum
 
 
 class MSGraph(Graph):
-    """The object describes the MindSpore graph, and it is defined in the anf_if proto file."""
+    """The object describes the MindSpore graph, and it is defined in the anf_ir proto file."""
 
-    def build_graph(self, graph_proto):
+    def build_graph(self, proto_data):
         """
-        Build graph by graph proto which refer to `anf_ir_pb2.GraphProto`, and set status to loading.
+        Build graph by graph proto which refer to `anf_ir_pb2.GraphProto`.
 
         Args:
-            graph_proto (anf_ir_pb2.GraphProto): Refer to `anf_ir_pb2.GraphProto`.
+            proto_data (anf_ir_pb2.GraphProto): Refer to `anf_ir_pb2.GraphProto`.
         """
-        logger.info("Start to build graph.")
+        logger.info("Start to build graph, graph name: %s.", proto_data.name)
+        start_time = time.time()
 
-        self._build_leaf_nodes(graph_proto)
-        self._build_polymeric_nodes()
-        self._build_name_scope_nodes()
-        self._update_polymeric_input_output()
-        logger.info("Build graph end, normal node count: %s, polymeric node "
-                    "count: %s.", len(self._normal_nodes), len(self._polymeric_nodes))
+        super(MSGraph, self).build_graph(proto_data)
 
-    def _build_leaf_nodes(self, graph_proto):
-        """
-        Build leaf node from graph proto.
+        precision = 6
+        time_consuming = round(time.time()-start_time, precision)
+        logger.info("Build graph end, all node count: %s, const count: %s, parameter count: %s, time-consuming: %s s.",
+                    self.normal_node_count, len(self._const_node_temp_cache),
+                    len(self._parameter_node_temp_cache), time_consuming)
 
-        Left node will contain operation node, parameter node, const node.
+    def _parse_data(self, proto_data):
+        """
+        The proto data is parsed and all nodes are stored in the specified structure.
 
         Args:
-            graph_proto (anf_ir_pb2.model_proto.graph): Refer to anf_ir_pb2.model_proto.graph.
+            proto_data (anf_ir_pb2.GraphProto): Refer to anf_ir_pb2.GraphProto object.
         """
-        logger.info("Start to build leaf nodes.")
-        leaf_node_id_map_name = {}
-        const_nodes_map = {}
+        logger.info("Start to parse graph proto data.")
 
-        for node_def in graph_proto.node:
-            if not node_def.name:
-                logger.warning("Finding a node with an empty name will not save it.")
-                continue
-            node = self._parse_graph_proto_node(node_def)
-            leaf_node_id_map_name.update({node.node_id: node.name})
+        self._parse_op_nodes(proto_data.node)
+        self._parse_parameters(proto_data.parameters)
+        self._parse_consts(proto_data.const_vals)
 
-        for parameter in graph_proto.parameters:
-            if not parameter.name:
-                logger.warning("Finding a parameter with an empty name will not save it.")
-                continue
-            node = self._parse_graph_proto_parameter(parameter)
-            const_nodes_map.update({node.name: node})
+        self._update_input_after_create_node()
+        self._update_output_after_create_node()
 
-        for i, const in enumerate(graph_proto.const_vals):
-            if not const.key:
-                logger.warning("Finding a const with an empty key will not save it.")
-                continue
-            node_id = 'const_{}'.format(i)
-            node = self._parse_graph_proto_const(const, node_id)
-            const_nodes_map.update({const.key: node})
+        logger.info("Parse proto data end, normal node count(only contain op node, "
+                    "parameter, const): %s.", self.normal_node_count)
 
-        self._calc_input(leaf_node_id_map_name, graph_proto, const_nodes_map)
-        self._calc_output()
-
-        logger.info("Build leaf nodes end, normal nodes count: %s, group count: %s, "
-                    "leaf nodes count: %s.", len(self._normal_nodes), len(self._node_groups),
-                    len(self._leaf_nodes))
-
-    def _calc_input(self, leaf_node_id_map_name, graph_proto, const_nodes_map):
+    def _parse_op_nodes(self, node_protos):
         """
-        Calc input for every leaf node.
+        Parse `anf_ir_pb2.NodeProto` object, and create a normal node.
 
         Args:
-            leaf_node_id_map_name (dict[str, str]): Format is {'node_id': 'node_name'}.
-            graph_proto (anf_ir_pb2.model_proto.graph): See anf_ir_pb2.model_proto.graph.
-            const_nodes_map (dict[str, Node]): Format is {'node name': <Const node>}.
+            node_protos (list[anf_ir_pb2.NodeProto]): Refer to anf_ir_pb2.NodeProto.
         """
-        logger.debug("Start to calc input.")
-        for node_def in graph_proto.node:
-            if not node_def.name:
-                logger.debug("The node name is empty, ignore it.")
+        logger.debug("Start to parse op nodes from proto.")
+        for node_proto in node_protos:
+            if not node_proto.name:
+                logger.warning("Finding a node with an empty name will not save it.")
                 continue
-            node_name = leaf_node_id_map_name[node_def.name]
-            node = self._leaf_nodes[node_name]
-            for input_def in node_def.input:
-                if not input_def.name:
-                    logger.warning("The input node name is empty, ignore it. node name: %s.", node_name)
-                    continue
 
-                edge_type = EdgeTypeEnum.DATA.value
-                if input_def.type == "CONTROL_EDGE":
-                    edge_type = EdgeTypeEnum.CONTROL.value
-
-                if const_nodes_map.get(input_def.name):
-                    const_node = copy.deepcopy(const_nodes_map[input_def.name])
-                    src_name = '{}/{}'.format(node.name_scope, input_def.name)
-                    if not self._normal_nodes.get(src_name):
-                        const_node.name = src_name
-                        const_node.name_scope = node.name_scope
-                        self._normal_nodes.update({src_name: const_node})
-                        self._leaf_nodes.update({src_name: const_node})
-                    src_node = self._leaf_nodes.get(src_name)
-                else:
-                    src_name = leaf_node_id_map_name.get(input_def.name)
-                    if not src_name:
-                        logger.warning("The input_def name '%s' in node '%s' is invalid, "
-                                       "will be ignore.", input_def.name, node_name)
-                        continue
-
-                    src_node = self._leaf_nodes.get(src_name)
-                    if src_node is None:
-                        logger.warning("The input '%s' in node '%s' is not in "
-                                       "leaf nodes.", src_name, node_name)
-                        continue
-
-                input_item = {
-                    src_name: {
-                        "shape": src_node.shape,
-                        "edge_type": edge_type,
-                        "scope": NodeTypeEnum.NAME_SCOPE.value
-                    }
-                }
-                node.update_input(input_item)
-
-            if self._normal_nodes.get(node_name):
-                self._normal_nodes[node_name] = node
-            else:
-                group_name = self._create_group_name(node.name_scope, node.node_type, node.name)
-                self._node_groups[group_name][node.name] = node
-
-    def _calc_output(self):
-        """Calc output of every node."""
-        logger.debug("Start to calc output.")
-
-        for name, node in self._leaf_nodes.items():
-            if node.node_type == NodeTypeEnum.CONST.value:
-                continue
-            for src_name, input_attr in node.inputs.items():
-                src_node = self._leaf_nodes[src_name]
-                if src_node.node_type == NodeTypeEnum.CONST.value:
-                    continue
+            node_name = Node.create_node_name(scope=node_proto.scope,
+                                              base_name=f'{node_proto.op_type}{node_proto.name}')
+            node = Node(name=node_name, node_id=node_proto.name)
+            node.type = node_proto.op_type
+            logger.debug("Foreach graph proto nodes, node id: %s, node name: %s, node def name: %s, "
+                         "input count: %s", node.node_id, node.name, node_proto.name, len(node_proto.input))
 
-                if self._normal_nodes.get(src_name):
-                    self._normal_nodes[src_name].update_output({name: input_attr})
-                else:
-                    group_name = self._create_group_name(src_node.name_scope,
-                                                         src_node.node_type, src_node.name)
-                    self._node_groups[group_name][src_name].update_output({name: input_attr})
+            self._parse_attributes(node_proto.attribute, node)
+            self._parse_inputs(node_proto.input, node)
 
-    def _parse_graph_proto_node(self, node_def):
-        """
-        Parse `anf_ir_pb2.model_proto.graph.node_def`, and create a a node.
+            node.output_i = node_proto.output_i
+            node.scope = node_proto.scope
+            node.output_shape = self._get_shape_by_parse_type_proto(node_proto.output_type)
+            node.output_data_type = self._get_data_type_by_parse_type_proto(node_proto.output_type)
 
-        Args:
-            node_def (anf_ir_pb2.model_proto.graph.node_def): Refer to anf_ir_pb2.model_proto.graph.node_def.
+            self._cache_node(node)
 
-        Returns:
-            Node, a `Node` object.
-        """
-        node_name = '/'.join([node_def.scope, node_def.op_type]) + node_def.name \
-            if node_def.scope else node_def.op_type + node_def.name
-        node = Node(name=node_name, node_id=node_def.name)
-        node.node_type = node_def.op_type
-        logger.debug("Foreach graph proto nodes, node id: %s, node name: %s, node def name: %s, "
-                     "input count: %s", node.node_id, node.name, node_def.name, len(node_def.input))
-
-        for attr in node_def.attribute:
-            node.update_attr({attr.name: str(attr.value)})
-
-        node.output_i = node_def.output_i
-        node.name_scope = node_def.scope
-
-        output_type = node_def.output_type
-        shape = self._parse_type_proto(output_type)
-        node.shape = shape
-
-        self._leaf_nodes.update({node.name: node})
-        group_name = self._create_group_name(node.name_scope, node.node_type, node.name)
-        if group_name is not None:
-            node_dict = self._node_groups.get(group_name, {})
-            node_dict.update({node.name: node})
-            self._node_groups.update({group_name: node_dict})
-        else:
-            self._normal_nodes.update({node.name: node})
-
-        return node
-
-    def _parse_graph_proto_parameter(self, parameter):
+    def _parse_parameters(self, parameter_protos):
         """
-        Parse anf_ir_pb2.model_proto.graph.parameter, and create a parameter node.
+        Parse `anf_ir_pb2.ParameterProto` object, and create a parameter node.
 
         Args:
-            parameter (anf_ir_pb2.model_proto.graph.parameter): Refer to anf_ir_pb2.model_proto.graph.parameter.
-
-        Returns:
-            Node, a `Node` object.
+            parameter_protos (list[anf_ir_pb2.ParameterProto]): Refer to anf_ir_pb2.ParameterProto.
         """
-        node = Node(name=parameter.name, node_id=parameter.name)
-        node.node_type = NodeTypeEnum.PARAMETER.value
-        node.shape = self._parse_type_proto(parameter.type)
-        logger.debug("Foreach graph proto parameters, node id: %s, node name: %s, "
-                     "node def name: %s", node.node_id, node.name, parameter.name)
-        return node
-
-    def _parse_graph_proto_const(self, const, const_node_id):
+        logger.debug("Start to parse parameters from proto.")
+        for parameter in parameter_protos:
+            if not parameter.name:
+                logger.warning("Finding a parameter with an empty name will not save it.")
+                continue
+            node = Node(name=parameter.name, node_id=parameter.name)
+            node.type = NodeTypeEnum.PARAMETER.value
+            node.output_shape = self._get_shape_by_parse_type_proto(parameter.type)
+            attr = dict(
+                type=self._get_data_type_by_parse_type_proto(parameter.type),
+                shape=str(self._get_shape_by_parse_type_proto(parameter.type))
+            )
+            node.add_attr(attr)
+
+            self._cache_node(node)
+            logger.debug("Foreach graph proto parameters, node id: %s, node name: %s, "
+                         "node def name: %s", node.node_id, node.name, parameter.name)
+
+    def _parse_consts(self, consts):
         """
-        Parse anf_ir_pb2.model_proto.graph.const, and create a const node.
+        Parse `anf_ir_pb2.NameValueProto` object, and create a const node.
 
         Args:
-            const (anf_ir_pb2.model_proto.graph.const): Refer to anf_ir_pb2.model_proto.graph.const
-            const_node_id (str): The id of the new const node, it should be unique in graph.
-
-        Returns:
-            Node, a `Node` object.
+            consts (list[anf_ir_pb2.NameValueProto]): Refer to `anf_ir_pb2.NameValueProto` object.
         """
-        node = Node(name=const.key, node_id=const_node_id)
-        node.node_type = NodeTypeEnum.CONST.value
-        node.update_attr({const.key: str(const.value)})
-        if const.value.dtype == DataTypeEnum.DT_TENSOR.value:
-            shape = []
-            for dim in const.value.tensor_val.dims:
-                shape.append(dim)
-            node.shape = shape
-        return node
-
-    def _parse_type_proto(self, type_proto):
+        logger.debug("Start to parse consts from proto.")
+        for const in consts:
+            if not const.key:
+                logger.warning("Finding a const with an empty key will not save it.")
+                continue
+            node = Node(name=const.key, node_id=const.key)
+            node.type = NodeTypeEnum.CONST.value
+            node.add_attr({const.key: str(const.value)})
+            if const.value.dtype == DataType.DT_TENSOR:
+                shape = []
+                for dim in const.value.tensor_val.dims:
+                    shape.append(dim)
+                node.output_shape = shape
+
+            self._cache_node(node)
+
+    def _get_shape_by_parse_type_proto(self, type_proto):
         """
         Parse proto's `message TypeProto` to get shape information.
 
@@ -260,32 +159,113 @@ class MSGraph(Graph):
                 shapes.append(dim.size)
         if type_proto.HasField('sequence_type'):
             for elem_type in type_proto.sequence_type.elem_types:
-                shapes.append(self._parse_type_proto(elem_type))
+                shapes.append(self._get_shape_by_parse_type_proto(elem_type))
         return shapes
 
-    def _create_group_name(self, name_scope, node_type, node_name):
+    def _get_data_type_by_parse_type_proto(self, type_proto):
         """
-        Create group name by node name, name scope, node type.
+        Get data type by parse type proto object.
 
-        Only nodes that conform to the rules are aggregated.
+        The name of the DataType, refer to `anf_ir_pb2.DataType` object.
+        If data type is tensor or tuple, the data name we return is `data_type[element_type, element_type]`.
 
         Args:
-            name_scope (str): The node name scope.
-            node_type (str): The node type.
-            node_name (str): The node name.
+            type_proto (anf_ir_pb2.TypeProto): Refer to anf_ir_pb2.TypeProto.
 
         Returns:
-            Optional[str], if match the rules will return a group name, else return None.
+            str, the data type.
+
+        """
+        data_type_name = self._get_data_type_name_by_value(type_proto, type_proto.data_type, field_name='data_type')
+        if type_proto.data_type == DataType.DT_TENSOR:
+            tensor_type_proto = type_proto.tensor_type
+            value = type_proto.tensor_type.elem_type
+            elem_type_name = self._get_data_type_name_by_value(tensor_type_proto, value, field_name='elem_type')
+            return f'{data_type_name}[{elem_type_name}]'
+
+        if type_proto.data_type == DataType.DT_TUPLE:
+            data_types = []
+            for elem_type in type_proto.sequence_type.elem_types:
+                data_types.append(self._get_data_type_by_parse_type_proto(elem_type))
+            return f'{data_type_name}{str(data_types)}'
+
+        return data_type_name
+
+    def _parse_inputs(self, input_protos, node):
+        """
+        Parse `anf_ir_pb2.InputProto` object.
+
+        Args:
+            input_protos (list[anf_ir_pb2.InputProto]): Refer to `anf_ir_pb2.InputProto` object.
+            node (Node): Refer to `Node` object, it is used to log message and update input.
         """
-        group_types = ['Reshape', 'Variable']
-        pattern_names = r'.*?/Cast-op\d+'
+        for input_proto in input_protos:
+            if not input_proto.name:
+                logger.warning("The name in input proto of node(%s) is empty, will ignore.", node.name)
+                continue
+
+            edge_type = EdgeTypeEnum.DATA.value if not input_proto.type else EdgeTypeEnum.CONTROL.value
+
+            # Notice:
+            # 1. The name in the input proto is the node id of the Node object.
+            # 2. In the current step, the shape of source node cannot be obtained,
+            #    so it is set to empty list by default, and the next step will update it.
+            # 3. Same with scope, set the default value first.
+            input_attr = {
+                "shape": [],
+                "edge_type": edge_type,
+                "independent_layout": False,
+                'data_type': ''
+            }
+
+            node.add_input(src_name=input_proto.name, input_attr=input_attr)
 
-        if node_type in group_types:
-            group_name = name_scope + '/' + node_type if name_scope else node_type
-            return group_name
+    def _parse_attributes(self, attributes, node):
+        """
+        Parse `anf_ir_pb2.AttributeProto` object., and Filters large attribute values.
+
+        Args:
+            attributes (list[anf_ir_pb2.AttributeProto]): Refer to `anf_ir_pb2.AttributeProto` object.
+            node (Node): Refer to `Node` object, it is used to log message and update attr.
+        """
+        for attr in attributes:
+            if attr.value.ByteSize() > self.MAX_NODE_ATTRIBUTE_VALUE_BYTES:
+                message = f"The attribute value of node({node.name}) " \
+                          f"is over {self.MAX_NODE_ATTRIBUTE_VALUE_BYTES} Bytes, will ignore."
+                logger.info(message)
+                continue
+            node.add_attr({attr.name: str(attr.value)})
+
+    def _update_input_after_create_node(self):
+        """Update the input of node after create node."""
+        for node in self._normal_node_map.values():
+            for src_node_id, input_attr in dict(node.input).items():
+                node.delete_input(src_node_id)
+                if not self._is_node_exist(node_id=src_node_id):
+                    message = f"The input node could not be found by node id({src_node_id}) " \
+                              f"while updating the input of the node({node})"
+                    logger.warning(message)
+
+                    continue
+
+                src_node = self._get_normal_node(node_id=src_node_id)
+                input_attr['shape'] = src_node.output_shape
+                input_attr['data_type'] = src_node.output_data_type
+                node.add_input(src_name=src_node.name, input_attr=input_attr)
+
+    def _update_output_after_create_node(self):
+        """Update the output of node after create node."""
+        # Constants and parameter should not exist for input and output.
+        filtered_node = {NodeTypeEnum.CONST.value, NodeTypeEnum.PARAMETER.value}
+        for node in self._normal_node_map.values():
+            for src_name, input_attr in node.input.items():
+                src_node = self._get_normal_node(node_name=src_name)
+                if src_node.type in filtered_node:
+                    continue
 
-        if node_type == 'FrameworkOp' and re.search(pattern_names, node_name):
-            group_name = name_scope + '/' + 'Cast-op' if name_scope else 'Cast-op'
-            return group_name
+                src_node.add_output(node.name, input_attr)
 
-        return None
+    @staticmethod
+    def _get_data_type_name_by_value(data_type, value, field_name='data_type'):
+        """Get the data type name by the enum value, data_type refer to `DataType` object."""
+        return data_type.DESCRIPTOR.fields_by_name[field_name].enum_type.values_by_number[value].name
diff --git a/mindinsight/datavisual/data_transform/graph/node.py b/mindinsight/datavisual/data_transform/graph/node.py
index 354e05c2257abb75fc15d7944c2bd5200afbec53..c1212099f8cd790191872665e427da36582576cb 100644
--- a/mindinsight/datavisual/data_transform/graph/node.py
+++ b/mindinsight/datavisual/data_transform/graph/node.py
@@ -17,10 +17,11 @@ This file is used to define the node of graph and associated base types.
 """
 from enum import Enum
 
+
 class NodeTypeEnum(Enum):
     """Node type enum. The following types are new to our custom."""
     NAME_SCOPE = 'name_scope'
-    POLYMERIC_SCOPE = 'polymeric_scope'
+    AGGREGATION_SCOPE = 'aggregation_scope'
     PARAMETER = 'Parameter'
     CONST = 'Const'
 
@@ -36,32 +37,33 @@ class Node:
 
     def __init__(self, name, node_id):
         self._node_id = node_id
-        self._name = name
-        self._type = ""
+        self.name = name
+        self.type = ""
         self._attr = dict()
-        self._inputs = dict()
-        self._output_i = -1
-        self._outputs = {}
-        self._polymeric_inputs = {}
-        self._polymeric_outputs = {}
-        self._polymeric_scope_name = ""
-        self._subnode_count = 0
-        self._name_scope = ""
-        self.shape = []
+        self._input = dict()
+        self.output_i = 0
+        self._output = {}
+        self._proxy_input = {}
+        self._proxy_output = {}
+        self.subnode_count = 0
+        self.scope = ""
+        self.independent_layout = False
+        self.output_shape = []
+        self.output_data_type = ""
 
     def to_dict(self):
         """Converts the node object to dictionary format."""
         return {
-            'name': self._name,
-            'type': self._type,
+            'name': self.name,
+            'type': self.type,
             'attr': self._attr,
-            'input': self._inputs,
-            'output_i': self._output_i,
-            'output': self._outputs,
-            'polymeric_input': self._polymeric_inputs,
-            'polymeric_output': self._polymeric_outputs,
-            'subnode_count': self._subnode_count,
-            'polymeric_scope_name': self._polymeric_scope_name
+            'input': self._input,
+            'output': self._output,
+            'output_i': self.output_i,
+            'proxy_input': self._proxy_input,
+            'proxy_output': self._proxy_output,
+            'subnode_count': self.subnode_count,
+            'independent_layout': self.independent_layout
         }
 
     @property
@@ -69,32 +71,26 @@ class Node:
         """The id of this node, and id is unique in graph."""
         return self._node_id
 
-    @property
-    def name(self):
-        """Get node name."""
-        return self._name
-
-    @name.setter
-    def name(self, name):
-        """Set node name."""
-        self._name = name
+    @staticmethod
+    def create_node_name(scope, base_name):
+        """
+        The name of the node consists of the scope and the basic name.
 
-    @property
-    def node_type(self):
-        """Get node type."""
-        return self._type
+        Args:
+            scope (str): The scope of node, such as 'Default/Conv2D'
+            base_name (str): The base name of node, such as 'Add11'.
 
-    @node_type.setter
-    def node_type(self, node_type):
-        """Set node type."""
-        self._type = node_type
+        Returns:
+            str, a node name.
+        """
+        return f'{scope}/{base_name}' if scope else base_name
 
     @property
     def attr(self):
         """Get node attr."""
         return self._attr
 
-    def update_attr(self, attr_dict):
+    def add_attr(self, attr_dict):
         """
         Update node attr.
 
@@ -104,114 +100,122 @@ class Node:
         self._attr.update(attr_dict)
 
     @property
-    def inputs(self):
+    def input(self):
         """
         Get all input of current node.
 
         Returns:
-            dict[str, dict], format is {'<src_name>': {'shape': [], 'edge_type', 'scope'}}.
+            dict[str, dict], refer to the input attr.
         """
-        return self._inputs
+        return self._input
 
-    def update_input(self, input_dict):
+    def add_input(self, src_name, input_attr):
         """
         Update input.
 
         Args:
-            input_dict (dict[str, dict]): Key is a source node name, and the value is a dict.
+            src_name (stc): The source node name.
+            input_attr (dict): The attribute of the input.
 
                 - shape (list): The shape of input tensor.
                 - edge_type (str): The type of edge, optional value refer to `EdgeTypeEnum`.
-                - scope (str): The scope of this source node.
+                - data_type (str): The data type of the input.
+                - independent_layout (bool): Indicates whether the source nodes are laid out independently.
         """
-        self._inputs.update(input_dict)
+        self._input.update({src_name: input_attr})
 
-    @property
-    def output_i(self):
-        """The memory address of this node when it is in run time."""
-        return self._output_i
-
-    @output_i.setter
-    def output_i(self, output_i):
-        """Set memory address."""
-        self._output_i = output_i
-
-    @property
-    def polymeric_inputs(self):
+    def delete_input(self, src_name):
         """
-        The polymeric input is the input of the polymeric nodes.
+        Delete input attribute by the given source name.
 
-        Returns:
-            dict[str, dict], format is {'<src_name>': {'edge_type': '<value>'}}.
+        Args:
+            src_name (str): The source node name.
         """
-        return self._polymeric_inputs
-
-    def update_polymeric_input(self, polymeric_input):
-        """The polymeric input is the input of the polymeric nodes."""
-        self._polymeric_inputs.update(polymeric_input)
+        self._input.pop(src_name)
 
     @property
-    def outputs(self):
+    def output(self):
         """The output node of this node."""
-        return self._outputs
+        return self._output
 
-    def update_output(self, output):
+    def add_output(self, dst_name, output_attr):
         """
-        Update output node.
+        Add a output node to this node.
 
         Args:
-            output (dict[str, TypedDict('NodeType', {'type': str})]): Key is a dst node name, and value is a dict.
+            dst_name (str): The name of the output node.
+            output_attr (dict: Same as the input attribute.
+        """
+        self._output.update({dst_name: output_attr})
+
+    def delete_output(self, dst_name):
+        """
+        Delete a output node.
 
-                - type (str): The type of the dst node.
+        Args:
+            dst_name (str): The name of the node to be deleted.
         """
-        self._outputs.update(output)
+        self._output.pop(dst_name)
 
     @property
-    def polymeric_outputs(self):
-        """Get polymeric output."""
-        return self._polymeric_outputs
+    def proxy_input(self):
+        """Return proxy input, type is dict."""
+        return self._proxy_input
 
-    def update_polymeric_output(self, polymeric_output):
+    def add_proxy_input(self, src_name, attr):
         """
-        Update polymeric output.
+        Add a proxy input to node.
 
         Args:
-            polymeric_output (dict[str, dict): Key is the polymeric scope name of dst name, and value is dict.
-
-                - edge_type (str): The edge type of the dst node.
+            src_name (str): The name of the input node.
+            attr (dict): The attr of the input.
 
+            - edge_type (str): The edge type, refer to `EdgeTypeEnum`.
         """
-        self._polymeric_outputs.update(polymeric_output)
+        self._proxy_input.update({src_name: attr})
+
+    def delete_proxy_input(self, src_name):
+        """Delete a proxy input by the src name."""
+        self._proxy_input.pop(src_name)
 
     @property
-    def polymeric_scope_name(self):
-        """Get polymeric scope name."""
-        return self._polymeric_scope_name
+    def proxy_output(self):
+        """Get proxy output, data type is dict."""
+        return self._proxy_output
 
-    @polymeric_scope_name.setter
-    def polymeric_scope_name(self, name):
-        """Set polymeric scope name."""
-        self._polymeric_scope_name = name
+    def add_proxy_output(self, dst_name, attr):
+        """
+        Add a proxy output to node.
 
-    @property
-    def subnode_count(self):
-        """The sub node count of this node, if this node is a scope node, this count will not be zero."""
-        return self._subnode_count
+        Args:
+            dst_name (str): The name of the output node.
+            attr (dict): The attr of the output.
 
-    @subnode_count.setter
-    def subnode_count(self, count):
-        """Set sub node count."""
-        self._subnode_count = count
+            - edge_type (str): The edge type, refer to `EdgeTypeEnum`.
+        """
+        self._proxy_output.update({dst_name: attr})
 
-    @property
-    def name_scope(self):
-        """Get name scope of this node."""
-        return self._name_scope
+    def delete_proxy_output(self, dst_name):
+        """Delete a proxy output by dst name."""
+        self._proxy_output.pop(dst_name)
 
-    @name_scope.setter
-    def name_scope(self, name_scope):
-        """Set name scope."""
-        self._name_scope = name_scope
+    @staticmethod
+    def copy_node_without_input_output(src_node, dst_node):
+        """
+        Copy a source node attribute to a new node, but not input and output.
+
+        Args:
+            src_node (Node): The copied node.
+            dst_node (Node): The destination node.
+        """
+        dst_node.type = src_node.type
+        dst_node.output_i = src_node.output_i
+        dst_node.subnode_count = src_node.subnode_count
+        dst_node.scope = src_node.scope
+        dst_node.independent_layout = src_node.independent_layout
+        dst_node.output_shape = src_node.output_shape
+        dst_node.output_data_type = src_node.output_data_type
+        dst_node.add_attr(src_node.attr)
 
     def __str__(self):
-        return f'<Node, name: {self._name}, type: {self._type}>'
+        return f'<Node, name: {self.name}, type: {self.type}>'
diff --git a/mindinsight/datavisual/processors/graph_processor.py b/mindinsight/datavisual/processors/graph_processor.py
index 7e596fab62a6c9dfebfde74badddc9519c61cc28..0521578af5b4a5746ec42c1cd296c10c1f16317a 100644
--- a/mindinsight/datavisual/processors/graph_processor.py
+++ b/mindinsight/datavisual/processors/graph_processor.py
@@ -20,9 +20,7 @@ and the status of graph will be checked before calling `Graph` object.
 from mindinsight.datavisual.common import exceptions
 from mindinsight.datavisual.common.enums import PluginNameEnum
 from mindinsight.datavisual.common.validation import Validation
-from mindinsight.datavisual.data_transform.graph import NodeTypeEnum
 from mindinsight.datavisual.processors.base_processor import BaseProcessor
-from mindinsight.utils.exceptions import ParamValueError
 from mindinsight.datavisual.common.exceptions import NodeNotInGraphError
 
 
@@ -51,13 +49,12 @@ class GraphProcessor(BaseProcessor):
         tensors = self._data_manager.list_tensors(train_id, tag=tag)
         self._graph = tensors[0].value
 
-    def get_nodes(self, name, node_type):
+    def list_nodes(self, scope):
         """
         Get the nodes of every layer in graph.
 
         Args:
-            name (str): The name of a node.
-            node_type (Any): The type of node, either 'name_scope' or 'polymeric'.
+            scope (str): The name of a scope.
 
         Returns:
             TypedDict('Nodes', {'nodes': list[Node]}), format is {'nodes': [<Node object>]}.
@@ -81,33 +78,19 @@ class GraphProcessor(BaseProcessor):
                             }
                           },
                           "output_i" : -1,
-                          "polymeric_input" : {},
-                          "polymeric_output" : {},
-                          "polymeric_scope_name" : "",
+                          "proxy_input" : {},
+                          "proxy_output" : {},
+                          "independent_layout" : False,
                           "subnode_count" : 0,
                           "type" : "Data"
                         }
                       ]
                     }
         """
-        if node_type not in [NodeTypeEnum.NAME_SCOPE.value, NodeTypeEnum.POLYMERIC_SCOPE.value]:
-            raise ParamValueError(
-                'The node type is not support, only either %s or %s.'
-                '' % (NodeTypeEnum.NAME_SCOPE.value, NodeTypeEnum.POLYMERIC_SCOPE.value))
-
-        if name and not self._graph.exist_node(name):
-            raise NodeNotInGraphError(node_name=name, node_type=node_type)
-        nodes = []
-        if node_type == NodeTypeEnum.NAME_SCOPE.value:
-            nodes = self._graph.get_normal_nodes(name)
-
-        if node_type == NodeTypeEnum.POLYMERIC_SCOPE.value:
-            if not name:
-                raise NodeNotInGraphError(node_name=name, node_type=node_type)
-
-            polymeric_scope_name = name
-            nodes = self._graph.get_polymeric_nodes(polymeric_scope_name)
+        if scope and not self._graph.exist_node(scope):
+            raise NodeNotInGraphError(node_name=scope)
 
+        nodes = self._graph.list_node_by_scope(scope=scope)
         return {'nodes': nodes}
 
     def search_node_names(self, search_content, offset, limit):
diff --git a/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result1.json b/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result1.json
index e9c54d67b1f13a440b615cd5992dda2d7b8598b9..3f58ddf2f7c4d56ded6e33bb941b68520b2eaf8c 100644
--- a/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result1.json
+++ b/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result1.json
@@ -1 +1 @@
-{"nodes":[{"attr":{},"input":{},"name":"Default/conv1-Conv2d","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":1,"type":"name_scope"},{"attr":{},"input":{},"name":"Default/bn1-BatchNorm2d","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":14,"type":"name_scope"},{"attr":{},"input":{},"name":"Default/bn1","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":20,"type":"name_scope"}]}
+{"nodes":[{"attr":{},"independent_layout":false,"input":{},"name":"Default","output":{},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":3,"type":"name_scope"}]}
\ No newline at end of file
diff --git a/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result2.json b/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result2.json
index adfe1106720ae6090057b37404bd5e09c66be76b..f356f95d05b111b154fbad917f75c8b555341ac4 100644
--- a/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result2.json
+++ b/tests/st/func/datavisual/graph/graph_results/test_query_nodes_success_result2.json
@@ -1 +1 @@
-{"nodes":[{"attr":{},"input":{},"name":"Default","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":3,"type":"name_scope"}]}
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@@ -1 +1 @@
-{"names":["Default/bn1/Reshape1","Default/bn1/Reshape10","Default/bn1/Reshape11","Default/bn1/Reshape12","Default/bn1/Reshape2","Default/bn1/Reshape3","Default/bn1/Reshape4","Default/bn1/Reshape5","Default/bn1/Reshape6","Default/bn1/Reshape7","Default/bn1/Reshape8","Default/bn1/Reshape9","Default/bn1/Reshape_1_[12]"]}
+{"names":["Default/bn1","Default/bn1-BatchNorm2d","Default/bn1-BatchNorm2d/Parameter[22]_3","Default/bn1-BatchNorm2d/Parameter[22]_3/conv1.weight","Default/bn1-BatchNorm2d/Parameter[22]_3/x","Default/bn1-BatchNorm2d/Parameter[22]_3/x1","Default/bn1-BatchNorm2d/Parameter[22]_3/x10","Default/bn1-BatchNorm2d/Parameter[22]_3/x11","Default/bn1-BatchNorm2d/Parameter[22]_3/x12","Default/bn1-BatchNorm2d/Parameter[22]_3/x13","Default/bn1-BatchNorm2d/Parameter[22]_3/x14","Default/bn1-BatchNorm2d/Parameter[22]_3/x15","Default/bn1-BatchNorm2d/Parameter[22]_3/x16","Default/bn1-BatchNorm2d/Parameter[22]_3/x17","Default/bn1-BatchNorm2d/Parameter[22]_3/x18","Default/bn1-BatchNorm2d/Parameter[22]_3/x19","Default/bn1-BatchNorm2d/Parameter[22]_3/x2","Default/bn1-BatchNorm2d/Parameter[22]_3/x20","Default/bn1-BatchNorm2d/Parameter[22]_3/x3","Default/bn1-BatchNorm2d/Parameter[22]_3/x4","Default/bn1-BatchNorm2d/Parameter[22]_3/x5","Default/bn1-BatchNorm2d/Parameter[22]_3/x6","Default/bn1-BatchNorm2d/Parameter[22]_3/x7","Default/bn1-BatchNorm2d/Parameter[22]_3/x8","Default/bn1-BatchNorm2d/Parameter[22]_3/x9","Default/bn1-BatchNorm2d/cst13","Default/bn1-BatchNorm2d/cst25","Default/bn1-BatchNorm2d/tuple_getitem105","Default/bn1-BatchNorm2d/tuple_getitem56","Default/bn1/Add[5]_0","Default/bn1/Add[5]_0/Add50","Default/bn1/Add[5]_0/Add51","Default/bn1/Add[5]_0/Add52","Default/bn1/Add[5]_0/Add53","Default/bn1/Add[5]_0/Add54","Default/bn1/Reshape[12]_1","Default/bn1/Reshape[12]_1/Reshape1","Default/bn1/Reshape[12]_1/Reshape10","Default/bn1/Reshape[12]_1/Reshape11","Default/bn1/Reshape[12]_1/Reshape12","Default/bn1/Reshape[12]_1/Reshape2","Default/bn1/Reshape[12]_1/Reshape3","Default/bn1/Reshape[12]_1/Reshape4","Default/bn1/Reshape[12]_1/Reshape5","Default/bn1/Reshape[12]_1/Reshape6","Default/bn1/Reshape[12]_1/Reshape7","Default/bn1/Reshape[12]_1/Reshape8","Default/bn1/Reshape[12]_1/Reshape9","Default/bn1/x","Default/bn1/x11"]}
\ No newline at end of file
diff --git a/tests/st/func/datavisual/graph/test_query_nodes_restful_api.py b/tests/st/func/datavisual/graph/test_query_nodes_restful_api.py
index 9e86baae1ac5d9ebbfdbeae1f67a193656bbdcb2..460893088825d5de5cb8cc0c5217c2289e97f4a8 100644
--- a/tests/st/func/datavisual/graph/test_query_nodes_restful_api.py
+++ b/tests/st/func/datavisual/graph/test_query_nodes_restful_api.py
@@ -40,22 +40,17 @@ class TestQueryNodes:
     @pytest.mark.platform_x86_gpu_training
     @pytest.mark.platform_x86_ascend_training
     @pytest.mark.usefixtures("init_summary_logs")
-    @pytest.mark.parametrize("node_name, node_type, result_file", [
-        ('', "name_scope", "test_query_nodes_success_result2.json"),
-        ("Default", "name_scope", "test_query_nodes_success_result1.json"),
-        ("Default/bn1/Reshape_1_[12]", "polymeric_scope", "test_query_nodes_success_result3.json")
+    @pytest.mark.parametrize("node_name, result_file", [
+        ('', "test_query_nodes_success_result1.json"),
+        ("Default", "test_query_nodes_success_result2.json"),
+        ("Default/bn1/Reshape[12]_1", "test_query_nodes_success_result3.json")
     ])
-    def test_query_namescope_success(self, client, node_name, node_type, result_file):
+    def test_list_node_success(self, client, node_name, result_file):
         """Query the name scope node."""
         train_id = gbl.get_train_ids()[0]
 
-        if node_name:
-            params = dict(train_id=train_id,
-                          type=node_type,
-                          name=node_name)
-        else:
-            params = dict(train_id=train_id,
-                          type=node_type)
+        params = dict(train_id=train_id,
+                      name=node_name)
         url = get_url(BASE_URL, params)
         response = client.get(url)
         assert response.status_code == 200
diff --git a/tests/st/func/datavisual/graph/test_query_single_nodes_restful_api.py b/tests/st/func/datavisual/graph/test_query_single_nodes_restful_api.py
index e93420fa3a961c2a7a405299ca6f2ad4c39df1c0..10807dfc04971e8387dc1555896b7f53ab0f1ed1 100644
--- a/tests/st/func/datavisual/graph/test_query_single_nodes_restful_api.py
+++ b/tests/st/func/datavisual/graph/test_query_single_nodes_restful_api.py
@@ -41,7 +41,7 @@ class TestQuerySingleNode:
     @pytest.mark.platform_x86_ascend_training
     @pytest.mark.usefixtures("init_summary_logs")
     @pytest.mark.parametrize("node_name, result_file", [
-        ('Default/bn1/Reshape1', "test_query_single_node_success_result1.json")
+        ('Default/bn1', "test_query_single_node_success_result1.json")
     ])
     def test_query_single_node_success(self, client, node_name, result_file):
         """Query single node."""
diff --git a/tests/st/func/datavisual/graph/test_search_nodes_restful_api.py b/tests/st/func/datavisual/graph/test_search_nodes_restful_api.py
index 3b0365b11ab902b83b1e25ca13cabb5e461e15e2..13a33ea4cd2a0dbec3c8c427e9455e6adecbe633 100644
--- a/tests/st/func/datavisual/graph/test_search_nodes_restful_api.py
+++ b/tests/st/func/datavisual/graph/test_search_nodes_restful_api.py
@@ -41,7 +41,7 @@ class TestSearchNodes:
     @pytest.mark.platform_x86_ascend_training
     @pytest.mark.usefixtures("init_summary_logs")
     @pytest.mark.parametrize("search_content, offset, limit, result_file", [
-        ('Default/bn1/Reshape', 0, 1000, "test_search_nodes_success_result1.json")
+        ('Default/bn1', 0, 1000, "test_search_nodes_success_result1.json")
     ])
     def test_search_nodes_success(self, client, search_content, offset, limit, result_file):
         """Search node with parameters: offset is 0, limit is 1000."""
diff --git a/tests/ut/backend/datavisual/test_train_visual_api.py b/tests/ut/backend/datavisual/test_train_visual_api.py
index 08d56a25a5ce2a2b914e3390ce0ed7450af9e078..6966170a879f5bd1a8635bd1b530770a68bd144c 100644
--- a/tests/ut/backend/datavisual/test_train_visual_api.py
+++ b/tests/ut/backend/datavisual/test_train_visual_api.py
@@ -22,7 +22,6 @@ from unittest.mock import Mock, patch
 
 import pytest
 
-from mindinsight.datavisual.data_transform.graph import NodeTypeEnum
 from mindinsight.datavisual.processors.graph_processor import GraphProcessor
 from mindinsight.datavisual.processors.images_processor import ImageProcessor
 from mindinsight.datavisual.processors.scalars_processor import ScalarsProcessor
@@ -227,47 +226,27 @@ class TestTrainVisual:
         assert results['error_msg'] == "Param missing. 'train_id' is required."
 
     @patch.object(GraphProcessor, '__init__')
-    def test_graph_nodes_with_type_is_invalid(self, mock_graph_processor, client):
-        """Test getting graph nodes with invalid type."""
-        mock_init = Mock(return_value=None)
-        mock_graph_processor.side_effect = mock_init
-
-        node_type = "invalid_node_type"
-        params = dict(train_id='aaa', type=node_type)
-        url = get_url(TRAIN_ROUTES['graph_nodes'], params)
-
-        response = client.get(url)
-        results = response.get_json()
-        assert response.status_code == 400
-        assert results['error_code'] == '50540002'
-        assert results['error_msg'] == "Invalid parameter value. The node type " \
-                                       "is not support, only either %s or %s." \
-                                       "" % (NodeTypeEnum.NAME_SCOPE.value,
-                                             NodeTypeEnum.POLYMERIC_SCOPE.value)
-
-    @patch.object(GraphProcessor, '__init__')
-    @patch.object(GraphProcessor, 'get_nodes')
-    def test_graph_nodes_success(self, mock_graph_processor, mock_graph_processor_1, client):
+    @patch.object(GraphProcessor, 'list_nodes')
+    def test_graph_nodes_success(self, mock_list_nodes_func, mock_init_func, client):
         """Test getting graph nodes successfully."""
 
-        def mock_get_nodes(name, node_type):
-            return dict(name=name, node_type=node_type)
+        def mock_list_nodes(scope):
+            return dict(scope=scope)
 
-        mock_graph_processor.side_effect = mock_get_nodes
+        mock_list_nodes_func.side_effect = mock_list_nodes
 
         mock_init = Mock(return_value=None)
-        mock_graph_processor_1.side_effect = mock_init
+        mock_init_func.side_effect = mock_init
 
         test_train_id = 'aaa'
         test_node_name = 'bbb'
-        test_node_type = NodeTypeEnum.NAME_SCOPE.value
-        params = dict(train_id=test_train_id, name=test_node_name, type=test_node_type)
+        params = dict(train_id=test_train_id, name=test_node_name)
         url = get_url(TRAIN_ROUTES['graph_nodes'], params)
 
         response = client.get(url)
         assert response.status_code == 200
         results = response.get_json()
-        assert results == dict(name=test_node_name, node_type=test_node_type)
+        assert results == dict(scope=test_node_name)
 
     def test_graph_node_names_with_train_id_is_none(self, client):
         """Test getting graph node names with train id is none."""
diff --git a/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results1.json b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results1.json
index 2b4c931a2ebc63f3f80c494c5b9d926c828432fa..3f58ddf2f7c4d56ded6e33bb941b68520b2eaf8c 100644
--- a/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results1.json
+++ b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results1.json
@@ -1,16 +1 @@
-{
-  "nodes" : [
-    {
-      "attr" : {},
-      "input" : {},
-      "name" : "Default",
-      "output" : {},
-      "output_i" : -1,
-      "polymeric_input" : {},
-      "polymeric_output" : {},
-      "polymeric_scope_name" : "",
-      "subnode_count" : 3,
-      "type" : "name_scope"
-    }
-  ]
-}
+{"nodes":[{"attr":{},"independent_layout":false,"input":{},"name":"Default","output":{},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":3,"type":"name_scope"}]}
\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results2.json b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results2.json
index 6ccfad3304c8c744e3ec35082c03dfd26576e1aa..06c6883a365186c2d5334d10f582dec31015654b 100644
--- a/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results2.json
+++ b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results2.json
@@ -1,20 +1 @@
-{
-  "nodes" : [
-    {
-      "attr" :
-      {
-        "output_names" : "dtype: DT_GRAPHS\nvalues {\n  dtype: DT_FLOAT64\n  str_val: \"output\"\n}\n",
-        "pad_mode" : "dtype: DT_FLOAT64\nstr_val: \"same\"\n"
-      },
-      "input" : {},
-      "name" : "Default/conv1-Conv2d/Conv2D1",
-      "output" : {},
-      "output_i" : 0,
-      "polymeric_input" : {},
-      "polymeric_output" : {},
-      "polymeric_scope_name" : "",
-      "subnode_count" : 0,
-      "type" : "Conv2D"
-    }
-  ]
-}
\ No newline at end of file
+{"nodes":[{"attr":{"output_names":"dtype: DT_GRAPHS\nvalues {\n  dtype: DT_FLOAT64\n  str_val: \"output\"\n}\n","pad_mode":"dtype: DT_FLOAT64\nstr_val: \"same\"\n"},"independent_layout":false,"input":{"Default/conv1-Conv2d/Parameter[12]_2/conv1.weight":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[64,3,7,7]},"Default/conv1-Conv2d/Parameter[12]_2/x":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x1":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x10":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x2":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x3":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x4":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x5":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x6":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x7":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x8":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/conv1-Conv2d/Parameter[12]_2/x9":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]}},"name":"Default/conv1-Conv2d/Conv2D55","output":{"Default/bn1/Reshape[12]_1/Reshape6":{"data_type":"DT_STRING","edge_type":"data","independent_layout":false,"shape":[1,64,112,112]}},"output_i":0,"proxy_input":{"Default/conv1-Conv2d/Parameter[12]_2":{"edge_type":"data"}},"proxy_output":{},"subnode_count":0,"type":"Conv2D"},{"attr":{},"independent_layout":true,"input":{},"name":"Default/conv1-Conv2d/Parameter[12]_2","output":{"Default/conv1-Conv2d/Conv2D55":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[64,3,7,7]}},"output_i":0,"proxy_input":{},"proxy_output":{"Default/conv1-Conv2d/Conv2D55":{"edge_type":"data"}},"subnode_count":12,"type":"aggregation_scope"}]}
\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results3.json b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results3.json
index 5fa3b784adc48ee71121f88ca68c1530833e1ecd..d76f713617645e457abb7a74be44b1d7efcdc83f 100644
--- a/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results3.json
+++ b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results3.json
@@ -1,437 +1 @@
-
-{
-  "nodes" : [
-    {
-      "attr" : {},
-      "input" :
-      {
-        "Default/bn1/Add50" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 1024, 14, 14]
-        },
-        "Default/bn1/conv1.weight" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [64, 3, 7, 7]
-        },
-        "Default/bn1/x" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x1" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x10" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x2" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x3" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x4" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x5" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x6" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x7" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x8" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        },
-        "Default/bn1/x9" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 3, 224, 224]
-        }
-      },
-      "name" : "Default/bn1/Reshape1",
-      "output" : {},
-      "output_i" : 0,
-      "polymeric_input" :
-      {
-        "Default/bn1/Add50" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/conv1.weight" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x1" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x10" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x2" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x3" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x4" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x5" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x6" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x7" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x8" :
-        {
-          "edge_type" : "data"
-        },
-        "Default/bn1/x9" :
-        {
-          "edge_type" : "data"
-        }
-      },
-      "polymeric_output" : {},
-      "polymeric_scope_name" : "Default/bn1/Reshape_1_[12]",
-      "subnode_count" : 0,
-      "type" : "Reshape"
-    },
-    {
-      "attr" : {},
-      "input" :
-      {
-        "Default/bn1/Add51" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 1024, 14, 14]
-        }
-      },
-      "name" : "Default/bn1/Reshape2",
-      "output" : {},
-      "output_i" : 0,
-      "polymeric_input" :
-      {
-        "Default/bn1/Add51" :
-        {
-          "edge_type" : "data"
-        }
-      },
-      "polymeric_output" : {},
-      "polymeric_scope_name" : "Default/bn1/Reshape_1_[12]",
-      "subnode_count" : 0,
-      "type" : "Reshape"
-    },
-    {
-      "attr" : {},
-      "input" :
-      {
-        "Default/bn1/Add52" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 1024, 14, 14]
-        }
-      },
-      "name" : "Default/bn1/Reshape3",
-      "output" : {},
-      "output_i" : 0,
-      "polymeric_input" :
-      {
-        "Default/bn1/Add52" :
-        {
-          "edge_type" : "data"
-        }
-      },
-      "polymeric_output" : {},
-      "polymeric_scope_name" : "Default/bn1/Reshape_1_[12]",
-      "subnode_count" : 0,
-      "type" : "Reshape"
-    },
-    {
-      "attr" : {},
-      "input" :
-      {
-        "Default/bn1/Add53" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 1024, 14, 14]
-        }
-      },
-      "name" : "Default/bn1/Reshape4",
-      "output" : {},
-      "output_i" : 0,
-      "polymeric_input" :
-      {
-        "Default/bn1/Add53" :
-        {
-          "edge_type" : "data"
-        }
-      },
-      "polymeric_output" : {},
-      "polymeric_scope_name" : "Default/bn1/Reshape_1_[12]",
-      "subnode_count" : 0,
-      "type" : "Reshape"
-    },
-    {
-      "attr" : {},
-      "input" :
-      {
-        "Default/bn1/Add54" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
-          "shape" : [1, 1024, 14, 14]
-        }
-      },
-      "name" : "Default/bn1/Reshape5",
-      "output" : {},
-      "output_i" : 0,
-      "polymeric_input" :
-      {
-        "Default/bn1/Add54" :
-        {
-          "edge_type" : "data"
-        }
-      },
-      "polymeric_output" : {},
-      "polymeric_scope_name" : "Default/bn1/Reshape_1_[12]",
-      "subnode_count" : 0,
-      "type" : "Reshape"
-    },
-    {
-      "attr" : {},
-      "input" :
-      {
-        "Default/bn1/x" :
-        {
-          "edge_type" : "data",
-          "scope" : "polymeric_scope",
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-      "subnode_count" : 0,
-      "type" : "Reshape"
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-  ]
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\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results4.json b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results4.json
new file mode 100644
index 0000000000000000000000000000000000000000..4a65daaafeb8f8f04ca1114c15586ce80364488e
--- /dev/null
+++ b/tests/ut/datavisual/processors/graph_results/test_get_nodes_success_expected_results4.json
@@ -0,0 +1 @@
+{"nodes":[{"attr":{},"independent_layout":false,"input":{"Default/bn1-BatchNorm2d/Parameter[22]_3/x":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x1":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x10":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x2":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x3":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x4":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x5":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x6":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x7":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x8":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x9":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/cst13":{"data_type":"","edge_type":"data","independent_layout":false,"shape":[]}},"name":"Default/bn1-BatchNorm2d/tuple_getitem56","output":{"Default/bn1/Add[5]_0/Add53":{"data_type":"DT_STRING","edge_type":"data","independent_layout":false,"shape":[1,128,28,28]}},"output_i":0,"proxy_input":{"Default/bn1-BatchNorm2d/Parameter[22]_3":{"edge_type":"data"}},"proxy_output":{},"subnode_count":0,"type":"tuple_getitem"},{"attr":{},"independent_layout":false,"input":{"Default/bn1-BatchNorm2d/Parameter[22]_3/conv1.weight":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[64,3,7,7]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x11":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x12":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x13":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x14":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x15":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x16":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x17":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x18":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x19":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/Parameter[22]_3/x20":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]},"Default/bn1-BatchNorm2d/cst25":{"data_type":"","edge_type":"data","independent_layout":false,"shape":[]}},"name":"Default/bn1-BatchNorm2d/tuple_getitem105","output":{},"output_i":0,"proxy_input":{"Default/bn1-BatchNorm2d/Parameter[22]_3":{"edge_type":"data"}},"proxy_output":{},"subnode_count":0,"type":"tuple_getitem"},{"attr":{},"independent_layout":true,"input":{},"name":"Default/bn1-BatchNorm2d/Parameter[22]_3","output":{"Default/bn1-BatchNorm2d/tuple_getitem105":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[64,3,7,7]},"Default/bn1-BatchNorm2d/tuple_getitem56":{"data_type":"","edge_type":"data","independent_layout":true,"shape":[1,3,224,224]}},"output_i":0,"proxy_input":{},"proxy_output":{"Default/bn1-BatchNorm2d/tuple_getitem105":{"edge_type":"data"},"Default/bn1-BatchNorm2d/tuple_getitem56":{"edge_type":"data"}},"subnode_count":22,"type":"aggregation_scope"},{"attr":{"cst13":"dtype: DT_INT32\nint_val: 0\n"},"independent_layout":false,"input":{},"name":"Default/bn1-BatchNorm2d/cst13","output":{"Default/bn1-BatchNorm2d/tuple_getitem56":{"data_type":"","edge_type":"data","independent_layout":false,"shape":[]}},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":0,"type":"Const"},{"attr":{"cst25":"dtype: DT_INT32\nint_val: 0\n"},"independent_layout":false,"input":{},"name":"Default/bn1-BatchNorm2d/cst25","output":{"Default/bn1-BatchNorm2d/tuple_getitem105":{"data_type":"","edge_type":"data","independent_layout":false,"shape":[]}},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":0,"type":"Const"}]}
\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_offset_expected_results1.json b/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_offset_expected_results1.json
index b57144054dcefebdd2fa42901e544ba1b6991e29..fcbe2e7c73cf3c4c9ec8b8637704e2e16a5dbaa6 100644
--- a/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_offset_expected_results1.json
+++ b/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_offset_expected_results1.json
@@ -1 +1 @@
-{"names":["Default/bn1-BatchNorm2d/cst25","Default/bn1-BatchNorm2d/tuple_getitem105","Default/bn1-BatchNorm2d/tuple_getitem53"]}
\ No newline at end of file
+{"names":["Default/bn1-BatchNorm2d/Parameter[22]_3/conv1.weight","Default/bn1-BatchNorm2d/Parameter[22]_3/x","Default/bn1-BatchNorm2d/Parameter[22]_3/x1"]}
\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results1.json b/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results1.json
index 4a7931d1479318829223ad352a128e72882404aa..2e47f1de4229ddd2494d97c24c944757d57c9fe3 100644
--- a/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results1.json
+++ b/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results1.json
@@ -1,4 +1 @@
-
-{
-  "names" : ["Default", "Default/bn1", "Default/bn1-BatchNorm2d", "Default/bn1-BatchNorm2d/conv1.weight", "Default/bn1-BatchNorm2d/cst25", "Default/bn1-BatchNorm2d/tuple_getitem105", "Default/bn1-BatchNorm2d/tuple_getitem53", "Default/bn1-BatchNorm2d/x11", "Default/bn1-BatchNorm2d/x12", "Default/bn1-BatchNorm2d/x13", "Default/bn1-BatchNorm2d/x14", "Default/bn1-BatchNorm2d/x15", "Default/bn1-BatchNorm2d/x16", "Default/bn1-BatchNorm2d/x17", "Default/bn1-BatchNorm2d/x18", "Default/bn1-BatchNorm2d/x19", "Default/bn1-BatchNorm2d/x20", "Default/bn1/Add50", "Default/bn1/Add51", "Default/bn1/Add52", "Default/bn1/Add53", "Default/bn1/Add54", "Default/bn1/Reshape1", "Default/bn1/Reshape10", "Default/bn1/Reshape11", "Default/bn1/Reshape12", "Default/bn1/Reshape2", "Default/bn1/Reshape3", "Default/bn1/Reshape4", "Default/bn1/Reshape5", "Default/bn1/Reshape6", "Default/bn1/Reshape7", "Default/bn1/Reshape8", "Default/bn1/Reshape9", "Default/bn1/Reshape_1_[12]", "Default/bn1/conv1.weight", "Default/bn1/cst13", "Default/bn1/x", "Default/bn1/x1", "Default/bn1/x10", "Default/bn1/x11", "Default/bn1/x2", "Default/bn1/x3", "Default/bn1/x4", "Default/bn1/x5", "Default/bn1/x6", "Default/bn1/x7", "Default/bn1/x8", "Default/bn1/x9", "Default/conv1-Conv2d", "Default/conv1-Conv2d/Conv2D1"]
-}
\ No newline at end of file
+{"names":["Default","Default/bn1","Default/bn1-BatchNorm2d","Default/bn1-BatchNorm2d/Parameter[22]_3","Default/bn1-BatchNorm2d/Parameter[22]_3/conv1.weight","Default/bn1-BatchNorm2d/Parameter[22]_3/x","Default/bn1-BatchNorm2d/Parameter[22]_3/x1","Default/bn1-BatchNorm2d/Parameter[22]_3/x10","Default/bn1-BatchNorm2d/Parameter[22]_3/x11","Default/bn1-BatchNorm2d/Parameter[22]_3/x12","Default/bn1-BatchNorm2d/Parameter[22]_3/x13","Default/bn1-BatchNorm2d/Parameter[22]_3/x14","Default/bn1-BatchNorm2d/Parameter[22]_3/x15","Default/bn1-BatchNorm2d/Parameter[22]_3/x16","Default/bn1-BatchNorm2d/Parameter[22]_3/x17","Default/bn1-BatchNorm2d/Parameter[22]_3/x18","Default/bn1-BatchNorm2d/Parameter[22]_3/x19","Default/bn1-BatchNorm2d/Parameter[22]_3/x2","Default/bn1-BatchNorm2d/Parameter[22]_3/x20","Default/bn1-BatchNorm2d/Parameter[22]_3/x3","Default/bn1-BatchNorm2d/Parameter[22]_3/x4","Default/bn1-BatchNorm2d/Parameter[22]_3/x5","Default/bn1-BatchNorm2d/Parameter[22]_3/x6","Default/bn1-BatchNorm2d/Parameter[22]_3/x7","Default/bn1-BatchNorm2d/Parameter[22]_3/x8","Default/bn1-BatchNorm2d/Parameter[22]_3/x9","Default/bn1-BatchNorm2d/cst13","Default/bn1-BatchNorm2d/cst25","Default/bn1-BatchNorm2d/tuple_getitem105","Default/bn1-BatchNorm2d/tuple_getitem56","Default/bn1/Add[5]_0","Default/bn1/Add[5]_0/Add50","Default/bn1/Add[5]_0/Add51","Default/bn1/Add[5]_0/Add52","Default/bn1/Add[5]_0/Add53","Default/bn1/Add[5]_0/Add54","Default/bn1/Reshape[12]_1","Default/bn1/Reshape[12]_1/Reshape1","Default/bn1/Reshape[12]_1/Reshape10","Default/bn1/Reshape[12]_1/Reshape11","Default/bn1/Reshape[12]_1/Reshape12","Default/bn1/Reshape[12]_1/Reshape2","Default/bn1/Reshape[12]_1/Reshape3","Default/bn1/Reshape[12]_1/Reshape4","Default/bn1/Reshape[12]_1/Reshape5","Default/bn1/Reshape[12]_1/Reshape6","Default/bn1/Reshape[12]_1/Reshape7","Default/bn1/Reshape[12]_1/Reshape8","Default/bn1/Reshape[12]_1/Reshape9","Default/bn1/x","Default/bn1/x11","Default/conv1-Conv2d","Default/conv1-Conv2d/Conv2D55","Default/conv1-Conv2d/Parameter[12]_2","Default/conv1-Conv2d/Parameter[12]_2/conv1.weight","Default/conv1-Conv2d/Parameter[12]_2/x","Default/conv1-Conv2d/Parameter[12]_2/x1","Default/conv1-Conv2d/Parameter[12]_2/x10","Default/conv1-Conv2d/Parameter[12]_2/x2","Default/conv1-Conv2d/Parameter[12]_2/x3","Default/conv1-Conv2d/Parameter[12]_2/x4","Default/conv1-Conv2d/Parameter[12]_2/x5","Default/conv1-Conv2d/Parameter[12]_2/x6","Default/conv1-Conv2d/Parameter[12]_2/x7","Default/conv1-Conv2d/Parameter[12]_2/x8","Default/conv1-Conv2d/Parameter[12]_2/x9"]}
\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results2.json b/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results2.json
index b98e912ab543d46e4b7afdce4c9041a1fff819a1..d8132275cf9f18ed1440413a9fc18c0b68fe3412 100644
--- a/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results2.json
+++ b/tests/ut/datavisual/processors/graph_results/test_search_node_names_with_search_content_expected_results2.json
@@ -1 +1 @@
-{"names":["Default/bn1","Default/bn1-BatchNorm2d","Default/bn1-BatchNorm2d/conv1.weight","Default/bn1-BatchNorm2d/cst25","Default/bn1-BatchNorm2d/tuple_getitem105","Default/bn1-BatchNorm2d/tuple_getitem53","Default/bn1-BatchNorm2d/x11","Default/bn1-BatchNorm2d/x12","Default/bn1-BatchNorm2d/x13","Default/bn1-BatchNorm2d/x14","Default/bn1-BatchNorm2d/x15","Default/bn1-BatchNorm2d/x16","Default/bn1-BatchNorm2d/x17","Default/bn1-BatchNorm2d/x18","Default/bn1-BatchNorm2d/x19","Default/bn1-BatchNorm2d/x20","Default/bn1/Add50","Default/bn1/Add51","Default/bn1/Add52","Default/bn1/Add53","Default/bn1/Add54","Default/bn1/Reshape1","Default/bn1/Reshape10","Default/bn1/Reshape11","Default/bn1/Reshape12","Default/bn1/Reshape2","Default/bn1/Reshape3","Default/bn1/Reshape4","Default/bn1/Reshape5","Default/bn1/Reshape6","Default/bn1/Reshape7","Default/bn1/Reshape8","Default/bn1/Reshape9","Default/bn1/Reshape_1_[12]","Default/bn1/conv1.weight","Default/bn1/cst13","Default/bn1/x","Default/bn1/x1","Default/bn1/x10","Default/bn1/x11","Default/bn1/x2","Default/bn1/x3","Default/bn1/x4","Default/bn1/x5","Default/bn1/x6","Default/bn1/x7","Default/bn1/x8","Default/bn1/x9"]}
+{"names":["Default/bn1","Default/bn1-BatchNorm2d","Default/bn1-BatchNorm2d/Parameter[22]_3","Default/bn1-BatchNorm2d/Parameter[22]_3/conv1.weight","Default/bn1-BatchNorm2d/Parameter[22]_3/x","Default/bn1-BatchNorm2d/Parameter[22]_3/x1","Default/bn1-BatchNorm2d/Parameter[22]_3/x10","Default/bn1-BatchNorm2d/Parameter[22]_3/x11","Default/bn1-BatchNorm2d/Parameter[22]_3/x12","Default/bn1-BatchNorm2d/Parameter[22]_3/x13","Default/bn1-BatchNorm2d/Parameter[22]_3/x14","Default/bn1-BatchNorm2d/Parameter[22]_3/x15","Default/bn1-BatchNorm2d/Parameter[22]_3/x16","Default/bn1-BatchNorm2d/Parameter[22]_3/x17","Default/bn1-BatchNorm2d/Parameter[22]_3/x18","Default/bn1-BatchNorm2d/Parameter[22]_3/x19","Default/bn1-BatchNorm2d/Parameter[22]_3/x2","Default/bn1-BatchNorm2d/Parameter[22]_3/x20","Default/bn1-BatchNorm2d/Parameter[22]_3/x3","Default/bn1-BatchNorm2d/Parameter[22]_3/x4","Default/bn1-BatchNorm2d/Parameter[22]_3/x5","Default/bn1-BatchNorm2d/Parameter[22]_3/x6","Default/bn1-BatchNorm2d/Parameter[22]_3/x7","Default/bn1-BatchNorm2d/Parameter[22]_3/x8","Default/bn1-BatchNorm2d/Parameter[22]_3/x9","Default/bn1-BatchNorm2d/cst13","Default/bn1-BatchNorm2d/cst25","Default/bn1-BatchNorm2d/tuple_getitem105","Default/bn1-BatchNorm2d/tuple_getitem56","Default/bn1/Add[5]_0","Default/bn1/Add[5]_0/Add50","Default/bn1/Add[5]_0/Add51","Default/bn1/Add[5]_0/Add52","Default/bn1/Add[5]_0/Add53","Default/bn1/Add[5]_0/Add54","Default/bn1/Reshape[12]_1","Default/bn1/Reshape[12]_1/Reshape1","Default/bn1/Reshape[12]_1/Reshape10","Default/bn1/Reshape[12]_1/Reshape11","Default/bn1/Reshape[12]_1/Reshape12","Default/bn1/Reshape[12]_1/Reshape2","Default/bn1/Reshape[12]_1/Reshape3","Default/bn1/Reshape[12]_1/Reshape4","Default/bn1/Reshape[12]_1/Reshape5","Default/bn1/Reshape[12]_1/Reshape6","Default/bn1/Reshape[12]_1/Reshape7","Default/bn1/Reshape[12]_1/Reshape8","Default/bn1/Reshape[12]_1/Reshape9","Default/bn1/x","Default/bn1/x11"]}
\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/graph_results/test_search_single_node_success_expected_results1.json b/tests/ut/datavisual/processors/graph_results/test_search_single_node_success_expected_results1.json
index 206cd26c08b4ba896508c17e85be477057d8286b..263bf2bf40ecb93c44dd0ad0aaef4906b0801a71 100644
--- a/tests/ut/datavisual/processors/graph_results/test_search_single_node_success_expected_results1.json
+++ b/tests/ut/datavisual/processors/graph_results/test_search_single_node_success_expected_results1.json
@@ -1 +1 @@
-{"children":{"children":{},"nodes":[{"attr":{},"input":{},"name":"Default/conv1-Conv2d","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":1,"type":"name_scope"},{"attr":{},"input":{},"name":"Default/bn1-BatchNorm2d","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":14,"type":"name_scope"},{"attr":{},"input":{},"name":"Default/bn1","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":20,"type":"name_scope"}],"scope_name":"Default"},"nodes":[{"attr":{},"input":{},"name":"Default","output":{},"output_i":-1,"polymeric_input":{},"polymeric_output":{},"polymeric_scope_name":"","subnode_count":3,"type":"name_scope"}],"scope_name":""}
+{"children":{"children":{},"nodes":[{"attr":{},"independent_layout":false,"input":{},"name":"Default/conv1-Conv2d","output":{"Default/bn1/Reshape[12]_1/Reshape6":{"data_type":"DT_STRING","edge_type":"data","independent_layout":false,"shape":[1,64,112,112]}},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":2,"type":"name_scope"},{"attr":{},"independent_layout":false,"input":{},"name":"Default/bn1-BatchNorm2d","output":{"Default/bn1/Add[5]_0/Add53":{"data_type":"DT_STRING","edge_type":"data","independent_layout":false,"shape":[1,128,28,28]}},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":5,"type":"name_scope"},{"attr":{},"independent_layout":false,"input":{"Default/bn1-BatchNorm2d/tuple_getitem56":{"data_type":"DT_STRING","edge_type":"data","independent_layout":false,"shape":[1,128,28,28]},"Default/conv1-Conv2d/Conv2D55":{"data_type":"DT_STRING","edge_type":"data","independent_layout":false,"shape":[1,64,112,112]}},"name":"Default/bn1","output":{},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":4,"type":"name_scope"}],"scope_name":"Default"},"nodes":[{"attr":{},"independent_layout":false,"input":{},"name":"Default","output":{},"output_i":0,"proxy_input":{},"proxy_output":{},"subnode_count":3,"type":"name_scope"}],"scope_name":""}
\ No newline at end of file
diff --git a/tests/ut/datavisual/processors/test_graph_processor.py b/tests/ut/datavisual/processors/test_graph_processor.py
index 87f72aae49ea8216380906fedb9108def5b3c937..678e459d9a459257bcf302d0b29ad0aa462b5a97 100644
--- a/tests/ut/datavisual/processors/test_graph_processor.py
+++ b/tests/ut/datavisual/processors/test_graph_processor.py
@@ -118,26 +118,27 @@ class TestGraphProcessor:
         assert mock_get_train_job_by_plugin.called
 
     @pytest.mark.usefixtures('load_graph_record')
-    @pytest.mark.parametrize("name, node_type", [("not_exist_name", "name_scope"), ("", "polymeric_scope")])
-    def test_get_nodes_with_not_exist_name(self, name, node_type):
+    @pytest.mark.parametrize("name", ["not_exist_name"])
+    def test_get_nodes_with_not_exist_name(self, name):
         """Test getting nodes with not exist name."""
         with pytest.raises(NodeNotInGraphError) as exc_info:
             graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
-            graph_processor.get_nodes(name, node_type)
+            graph_processor.list_nodes(name)
 
         assert 'Can not find node in graph by the given node name' in exc_info.value.message
 
     @pytest.mark.usefixtures('load_graph_record')
     @pytest.mark.parametrize(
-        "name, node_type, result_file",
-        [(None, 'name_scope', 'test_get_nodes_success_expected_results1.json'),
-         ('Default/conv1-Conv2d', 'name_scope', 'test_get_nodes_success_expected_results2.json'),
-         ('Default/bn1/Reshape_1_[12]', 'polymeric_scope', 'test_get_nodes_success_expected_results3.json')])
-    def test_get_nodes_success(self, name, node_type, result_file):
+        "name, result_file",
+        [(None, 'test_get_nodes_success_expected_results1.json'),
+         ('Default/conv1-Conv2d', 'test_get_nodes_success_expected_results2.json'),
+         ('Default/bn1/Reshape[12]_1', 'test_get_nodes_success_expected_results3.json'),
+         ('Default/bn1-BatchNorm2d', 'test_get_nodes_success_expected_results4.json'),
+         ])
+    def test_get_nodes_success(self, name, result_file):
         """Test getting nodes successfully."""
-
         graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
-        results = graph_processor.get_nodes(name, node_type)
+        results = graph_processor.list_nodes(name)
 
         expected_file_path = os.path.join(self.graph_results_dir, result_file)
         compare_result_with_file(results, expected_file_path)
diff --git a/tests/utils/log_generators/cyclic_graph.json b/tests/utils/log_generators/cyclic_graph.json
new file mode 100644
index 0000000000000000000000000000000000000000..08f4b504f9e2be0b05036511fe55f1427919131a
--- /dev/null
+++ b/tests/utils/log_generators/cyclic_graph.json
@@ -0,0 +1,640 @@
+{
+  "node": [
+    {
+      "input": [
+        {
+          "name": "data",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "1",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "1",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "2",
+      "opType": "Add",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "2",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "3",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "3",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "4",
+      "opType": "Add",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "4",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "5",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "5",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "6",
+      "opType": "Add",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "6",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "7",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "7",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "8",
+      "opType": "Add",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "8",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "9",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "9",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "10",
+      "opType": "Add",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "9",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "11",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "9",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "12",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "9",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "13",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "9",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "14",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "14",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "15",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "15",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "16",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    }
+  ],
+  "name": "849_848_847_424_1_construct",
+  "parameters": [
+    {
+      "name": "data",
+      "type": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              },
+              {
+                "size": "3"
+              },
+              {
+                "size": "224"
+              },
+              {
+                "size": "224"
+              }
+            ]
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/tests/utils/log_generators/graph_base.json b/tests/utils/log_generators/graph_base.json
index ff4d541669e88aa9f9d2aa1075390929ef7b57f7..6ba125cbe7102209961f0e9ad1314d861c0629b5 100644
--- a/tests/utils/log_generators/graph_base.json
+++ b/tests/utils/log_generators/graph_base.json
@@ -51,7 +51,7 @@
           "type": "DATA_EDGE"
         }
       ],
-      "name": "1",
+      "name": "55",
       "opType": "Conv2D",
       "scope": "Default/conv1-Conv2d",
       "attribute": [
@@ -149,7 +149,7 @@
           "type": "DATA_EDGE"
         }
       ],
-      "name": "53",
+      "name": "56",
       "opType": "tuple_getitem",
       "scope": "Default/bn1-BatchNorm2d",
       "outputType": {
@@ -354,7 +354,7 @@
     {
       "input": [
         {
-          "name": "x11",
+          "name": "56",
           "type": "DATA_EDGE"
         }
       ],
@@ -585,7 +585,7 @@
     {
       "input": [
         {
-          "name": "x",
+          "name": "55",
           "type": "DATA_EDGE"
         }
       ],
diff --git a/tests/utils/log_generators/high_degree_graph.json b/tests/utils/log_generators/high_degree_graph.json
new file mode 100644
index 0000000000000000000000000000000000000000..75347b6f27257d96eb44248bdff3c4a7ae4c0efd
--- /dev/null
+++ b/tests/utils/log_generators/high_degree_graph.json
@@ -0,0 +1,559 @@
+{
+  "node": [
+    {
+      "input": [
+        {
+          "name": "6",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "1",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "7",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "2",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "8",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "3",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "14",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "4",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "10",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "5",
+      "opType": "Conv2D",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "data",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "6",
+      "opType": "Add",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "data",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "7",
+      "opType": "Sub",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "data",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "8",
+      "opType": "Sqrt",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
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+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "5",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "9",
+      "opType": "Reshape",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
+            "dim": [
+              {
+                "size": "1"
+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
+        {
+          "name": "data",
+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "10",
+      "opType": "Mul",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
+          "elemType": "DT_FLOAT16",
+          "shape": {
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+              }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "input": [
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+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "11",
+      "opType": "Add",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
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+        "dataType": "DT_STRING",
+        "tensorType": {
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+          "type": "DATA_EDGE"
+        }
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+      "opType": "Relu",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
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+          "shape": {
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+              }
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+          }
+        }
+      }
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+    {
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+          "type": "DATA_EDGE"
+        }
+      ],
+      "name": "13",
+      "opType": "Dense",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
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+            "values": [
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+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
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+        "tensorType": {
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+            ]
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+        }
+      }
+    },
+    {
+      "input": [],
+      "name": "14",
+      "opType": "Reshape",
+      "scope": "Default",
+      "attribute": [
+        {
+          "name": "output_names",
+          "value": {
+            "dtype": "DT_GRAPHS",
+            "values": [
+              {
+                "dtype": "DT_FLOAT64",
+                "strVal": "output"
+              }
+            ]
+          }
+        }
+      ],
+      "outputType": {
+        "dataType": "DT_STRING",
+        "tensorType": {
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+          "shape": {
+            "dim": [
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+              }
+            ]
+          }
+        }
+      }
+    }
+  ],
+  "name": "849_848_847_424_1_construct",
+  "parameters": [
+    {
+      "name": "data",
+      "type": {
+        "dataType": "DT_STRING",
+        "tensorType": {
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+          "shape": {
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+              {
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+              }
+            ]
+          }
+        }
+      }
+    }
+  ]
+}