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.nn.GCNConv or just write a gcn layer with message passing interface.

import paddle
import paddle.nn as nn

class CustomGCNConv(nn.Layer):
    def __init__(self, input_size, output_size):
        super(GCNConv, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.linear = nn.Linear(input_size, output_size)
        self.norm = norm
        self.activation = activation

    def forward(self, graph, feature):
        norm = GF.degree_norm(graph)

        feature = self.linear(feature)

        output = graph.send_recv(feature, "sum")

        output = output * norm
        output = nn.functional.relu(output)
        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==2.0.0

• pgl==2.1

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.

# Run on GPU
CUDA_VISIBLE_DEVICES=0 python train.py --dataset cora

# Run on CPU
CUDA_VISIBLE_DEVICES= python train.py --dataset cora

Hyperparameters

• dataset: The citation dataset “cora”, “citeseer”, “pubmed”.