PGL Examples for GCN with StaticGraphWrapper

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.

However, different from the reproduction in examples/gcn, we use pgl.graph_wrapper.StaticGraphWrapper to preload the graph data into gpu or cpu memories which achieves better performance on speed.

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	epoch time	examples/gcn	Improvement
Cora	~81%	0.0047s	0.0104s	2.21x
Pubmed	~79%	0.0049s	0.0154s	3.14x
Citeseer	~71%	0.0045s	0.0177s	3.93x

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.