This repository contains a PyTorch implementation of "Simple and Deep Graph Convolutional Networks".(https://arxiv.org/abs/2007.02133)
- CUDA 10.1
- python 3.6.9
- pytorch 1.3.1
- networkx 2.1
- scikit-learn
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.
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 |
- 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
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
@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},
}