Graph Convolutional Networks in PyTorch

PyTorch 1.6 and Python 3.7 implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1].

Tested on the cora/pubmed/citeseer data set, the code on this repository can achieve the effect of the paper.

Benchmark

dataset	Citeseea	Cora	Pubmed
GCN(official)	70.3	81.5	79.0
This repo.	70.7	81.2	79.2

NOTE: The result of the experiment is to repeat the run 10 times, and then take the average of accuracy.

Requirements

• PyTorch==1.6.0
• Python==3.7
• dgl==0.5.2
• scipy==1.5.2
• numpy==1.19.1
• networkx==2.5

Usage

python train.py --dataset cora

References

[1] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016