GCN: Graph Convolutional Networks¶

Graph Convolutional Network (GCN) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce GCN algorithms and reach the same level of indicators as the paper in citation network benchmarks.

Simple example to build GCN¶

To build a gcn layer, one can use our pre-defined pgl.layers.gcn or just write a gcn layer with message passing interface.

import paddle.fluid as fluid
def gcn_layer(graph_wrapper, node_feature, hidden_size, act):
    def send_func(src_feat, dst_feat, edge_feat):
        return src_feat["h"]

    def recv_func(msg):
        return fluid.layers.sequence_pool(msg, "sum")

    message = graph_wrapper.send(send_func, nfeat_list=[("h", node_feature)])
    output = graph_wrapper.recv(recv_func, message)
    output = fluid.layers.fc(output, size=hidden_size, act=act)
    return output

Datasets¶

The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the paper.

Dependencies¶

paddlepaddle>=1.6
pgl

Performance¶

We train our models for 200 epochs and report the accuracy on the test dataset.

Dataset	Accuracy
Cora	~81%
Pubmed	~79%
Citeseer	~71%

How to run¶

For examples, use gpu to train gcn on cora dataset.

python train.py --dataset cora --use_cuda

Hyperparameters¶

dataset: The citation dataset “cora”, “citeseer”, “pubmed”.
use_cuda: Use gpu if assign use_cuda.