modify the README.md of deepergcn
-# GAT: Graph Attention Networks
-
-[Graph Attention Networks \(GAT\)](https://arxiv.org/abs/1710.10903) is a novel architectures that operate on graph-structured data, which leverages masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. Based on PGL, we reproduce GAT algorithms and reach the same level of indicators as the paper in citation network benchmarks.
-### Simple example to build single head GAT
-
-To build a gat layer, one can use our pre-defined ```pgl.layers.gat``` or just write a gat layer with message passing interface.
-```python
-import paddle.fluid as fluid
-def gat_layer(graph_wrapper, node_feature, hidden_size):
- def send_func(src_feat, dst_feat, edge_feat):
- logits = src_feat["a1"] + dst_feat["a2"]
- logits = fluid.layers.leaky_relu(logits, alpha=0.2)
- return {"logits": logits, "h": src_feat }
-
- def recv_func(msg):
- norm = fluid.layers.sequence_softmax(msg["logits"])
- output = msg["h"] * norm
- return output
-
- h = fluid.layers.fc(node_feature, hidden_size, bias_attr=False, name="hidden")
- a1 = fluid.layers.fc(node_feature, 1, name="a1_weight")
- a2 = fluid.layers.fc(node_feature, 1, name="a2_weight")
- message = graph_wrapper.send(send_func,
- nfeat_list=[("h", h), ("a1", a1), ("a2", a2)])
- output = graph_wrapper.recv(recv_func, message)
- return output
-```
+# DeeperGCN: All You Need to Train Deeper GCNs
+
+see more information in https://arxiv.org/pdf/2006.07739.pdf
### Datasets
@@ -36,16 +12,6 @@ The datasets contain three citation networks: CORA, PUBMED, CITESEER. The detail
- paddlepaddle>=1.6
- pgl
-### Performance
-
-We train our models for 200 epochs and report the accuracy on the test dataset.
-
-| Dataset | Accuracy |
-| --- | --- |
-| Cora | ~83% |
-| Pubmed | ~78% |
-| Citeseer | ~70% |
-
### How to run
For examples, use gpu to train gat on cora dataset.
