This API is mainly used in Graph Learning domain, which should be used
in conjunction with `graph_sample_neighbors` API. And the main purpose
is to reindex the ids information of the input nodes, and return the
in conjunction with `paddle.geometric.sample_neighbors` API. And the main purpose
is to reindex the ids information of the input nodes, and return the
corresponding graph edges after reindex.
**Notes**:
The number in x should be unique, otherwise it would cause potential errors.
We will reindex all the nodes from 0.
Take input nodes x = [0, 1, 2] as an example.
If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
then we know that the neighbors of 0 is [8, 9], the neighbors of 1
is [0, 4, 7], and the neighbors of 2 is [6, 7].
Then after graph_reindex, we will have 3 different outputs:
1. reindex_src: [3, 4, 0, 5, 6, 7, 6]
2. reindex_dst: [0, 0, 1, 1, 1, 2, 2]
3. out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]
We can see that the numbers in `reindex_src` and `reindex_dst` is the corresponding index
Take input nodes x = [0, 1, 2] as an example. If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7].
Then after graph_reindex, we will have 3 different outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6], reindex_dst: [0, 0, 1, 1, 1, 2, 2]
and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]. We can see that the numbers in `reindex_src` and `reindex_dst` is the corresponding index
of nodes in `out_nodes`.
Note:
The number in x should be unique, otherwise it would cause potential errors. We will reindex all the nodes from 0.
Args:
x (Tensor): The input nodes which we sample neighbors for. The available
data type is int32, int64.
neighbors (Tensor): The neighbors of the input nodes `x`. The data type
should be the same with `x`.
count (Tensor): The neighbor count of the input nodes `x`. And the
count (Tensor): The neighbor count of the input nodes `x`. And the
data type should be int32.
value_buffer (Tensor|None): Value buffer for hashtable. The data type should be int32,
and should be filled with -1. Only useful for gpu version.
index_buffer (Tensor|None): Index buffer for hashtable. The data type should be int32,
and should be filled with -1. Only useful for gpu version.
`value_buffer` and `index_buffer` should be both not None
`value_buffer` and `index_buffer` should be both not None
if you want to speed up by using hashtable buffer.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
reindex_src (Tensor): The source node index of graph edges after reindex.
reindex_dst (Tensor): The destination node index of graph edges after reindex.
out_nodes (Tensor): The index of unique input nodes and neighbors before reindex,
where we put the input nodes `x` in the front, and put neighbor
nodes in the back.
- reindex_src (Tensor), the source node index of graph edges after reindex.
Examples:
.. code-block:: python
- reindex_dst (Tensor), the destination node index of graph edges after reindex.
import paddle
- out_nodes (Tensor), the index of unique input nodes and neighbors before reindex, where we put the input nodes `x` in the front, and put neighbor nodes in the back.
Take input nodes x = [0, 1, 2] as an example. For graph A, suppose we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7]. For graph B,
suppose we have neighbors = [0, 2, 3, 5, 1], and count = [1, 3, 1], then we know that the neighbors of 0 is [0], the neighbors of 1 is [2, 3, 5],
and the neighbors of 3 is [1]. We will get following outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1], reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2]
and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5].
Note:
The number in x should be unique, otherwise it would cause potential errors. We support multi-edge-types neighbors reindexing in reindex_heter_graph api. We will reindex all the nodes from 0.
Args:
x (Tensor): The input nodes which we sample neighbors for. The available
data type is int32, int64.
neighbors (list|tuple): The neighbors of the input nodes `x` from different graphs.
neighbors (list|tuple): The neighbors of the input nodes `x` from different graphs.
The data type should be the same with `x`.
count (list|tuple): The neighbor counts of the input nodes `x` from different graphs.
count (list|tuple): The neighbor counts of the input nodes `x` from different graphs.
And the data type should be int32.
value_buffer (Tensor|None): Value buffer for hashtable. The data type should be int32,
and should be filled with -1. Only useful for gpu version.
...
...
@@ -188,48 +171,52 @@ def reindex_heter_graph(x,
For more information, please refer to :ref:`api_guide_Name`.
Returns:
reindex_src (Tensor): The source node index of graph edges after reindex.
reindex_dst (Tensor): The destination node index of graph edges after reindex.
out_nodes (Tensor): The index of unique input nodes and neighbors before reindex,
where we put the input nodes `x` in the front, and put neighbor
nodes in the back.
Examples:
- reindex_src (Tensor), the source node index of graph edges after reindex.
.. code-block:: python
- reindex_dst (Tensor), the destination node index of graph edges after reindex.
import paddle
- out_nodes (Tensor), the index of unique input nodes and neighbors before reindex,
where we put the input nodes `x` in the front, and put neighbor
