/GCNII

PyTorch implementation of "Simple and Deep Graph Convolutional Networks"

Primary LanguagePython

Simple and Deep Graph Convolutional Networks

PWC PWC PWC PWC

This repository contains a PyTorch implementation of "Simple and Deep Graph Convolutional Networks".(https://arxiv.org/abs/2007.02133)

Dependencies

  • CUDA 10.1
  • python 3.6.9
  • pytorch 1.3.1
  • networkx 2.1
  • scikit-learn

Datasets

The data folder contains three benchmark datasets(Cora, Citeseer, Pubmed), and the newdata folder contains four datasets(Chameleon, Cornell, Texas, Wisconsin) from Geom-GCN. We use the same semi-supervised setting as GCN and the same full-supervised setting as Geom-GCN. PPI can be downloaded from GraphSAGE.

Results

Testing accuracy summarized below.

Dataset Depth Metric Dataset Depth Metric
Cora 64 85.5 Cham 8 62.48
Cite 32 73.4 Corn 16 76.49
Pubm 16 80.3 Texa 32 77.84
Cora(full) 64 88.49 Wisc 16 81.57
Cite(full) 64 77.13 PPI 9 99.56
Pubm(full) 64 90.30 obgn-arxiv 16 72.74

Usage

  • To replicate the semi-supervised results, run the following script
sh semi.sh
  • To replicate the full-supervised results, run the following script
sh full.sh
  • To replicate the inductive results of PPI, run the following script
sh ppi.sh

Reference implementation

The PyG folder includes a simple PyTorch Geometric implementation of GCNII. Requirements: torch-geometric >= 1.5.0 and ogb >= 1.2.0.

  • Running examples
python cora.py
python arxiv.py

Citation

@article{chenWHDL2020gcnii,
  title = {Simple and Deep Graph Convolutional Networks},
  author = {Ming Chen, Zhewei Wei and Zengfeng Huang, Bolin Ding and Yaliang Li},
  year = {2020},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
}