Simple and Deep Graph Convolutional Networks

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},
}