SGC: Simplifying Graph Convolutional Networks

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

Datasets

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

Dependencies

• paddlepaddle 1.5

• pgl

Performance

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

Dataset	Accuracy	Speed with paddle 1.5 (epoch time)
Cora	0.818 (paper: 0.810)	0.0015s
Pubmed	0.788 (paper: 0.789)	0.0015s
Citeseer	0.719 (paper: 0.719)	0.0015s

How to run

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

python sgc.py --dataset cora --use_cuda

Hyperparameters

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

• use_cuda: Use gpu if assign use_cuda.