add appnp

pgl/layers/conv.py

@@ -18,7 +18,7 @@ import paddle.fluid as fluid
 from pgl.utils import paddle_helper
 from pgl import message_passing
 
-__all__ = ['gcn', 'gat', 'gin', 'gaan', 'gen_conv']
+__all__ = ['gcn', 'gat', 'gin', 'gaan', 'gen_conv', 'appnp']
 
 
 def gcn(gw, feature, hidden_size, activation, name, norm=None):
@@ -404,3 +404,40 @@ def gen_conv(gw,
 
     return output
 
+def appnp(gw, feature, norm=None, alpha=0.2, k_hop=10):
+    """Implementation of APPNP of "Predict then Propagate: Graph Neural Networks
+    meet Personalized PageRank"  (ICLR 2019). 
+
+    Args:
+        gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)
+
+        feature: A tensor with shape (num_nodes, feature_size).
+
+        norm: If :code:`norm` is not None, then the feature will be normalized. Norm must
+              be tensor with shape (num_nodes,) and dtype float32.
+
+        k_hop: K Steps for Propagation
+
+    Return:
+        A tensor with shape (num_nodes, hidden_size)
+    """
+
+    def send_src_copy(src_feat, dst_feat, edge_feat):
+       feature = src_feat["h"]
+       return feature
+
+    h0 = feature
+    
+    for i in range(k_hop):
+        if norm is not None:
+            feature = feature * norm
+
+        msg = gw.send(send_src_copy, nfeat_list=[("h", feature)])
+
+        feature = gw.recv(msg, "sum")
+
+        if norm is not None:
+            feature = feature * norm
+
+        feature = feature * (1 - alpha) + h0 * alpha
+    return feature