Pytorch-geometric implementation for KDD'22 paper: GraphMAE: Self-Supervised Masked Graph Autoencoders.

We also have a Chinese blog about GraphMAE on Zhihu (知乎), and an English Blog on Medium.

GraphMAE is a generative self-supervised graph learning method, which achieves competitive or better performance than existing contrastive methods on tasks including node classification, graph classification, and molecular property prediction.

• Python >= 3.7
• Pytorch >= 1.9.0
• pyyaml == 5.4.1
• Pytorch-geometric >= 2.2.0

For quick start, you could run the scripts:

Node classification

sh scripts/run_transductive.sh <dataset_name> <gpu_id> # for transductive node classification
# example: sh scripts/run_transductive.sh cora/citeseer/pubmed/ogbn-arxiv 0
sh scripts/run_inductive.sh <dataset_name> <gpu_id> # for inductive node classification
# example: sh scripts/run_inductive.sh reddit/ppi 0

# Or you could run the code manually:
# for transductive node classification
python main_transductive.py --dataset cora --encoder gat --decoder gat --seed 0 --device 0
# for inductive node classification
python main_inductive.py --dataset ppi --encoder gat --decoder gat --seed 0 --device 0

Supported datasets:

• transductive node classification: cora, citeseer, pubmed, ogbn-arxiv
• inductive node classification: ppi, reddit

Run the scripts provided or add --use_cfg in command to reproduce the reported results.

Graph classification

sh scripts/run_graph.sh <dataset_name> <gpu_id>
# example: sh scripts/run_graph.sh mutag/imdb-b/imdb-m/proteins/... 0 

# Or you could run the code manually:
python main_graph.py --dataset IMDB-BINARY --encoder gin --decoder gin --seed 0 --device 0

Supported datasets:

• IMDB-BINARY, IMDB-MULTI, PROTEINS, MUTAG, NCI1, REDDIT-BINERY, COLLAB

Run the scripts provided or add --use_cfg in command to reproduce the reported results.

Molecular Property Prediction

Please refer to codes in ./chem for molecular property prediction.

Datasets used in node classification and graph classification will be downloaded automatically using PyG's API when running the code.

If you find this work is helpful to your research, please consider citing our paper:

@inproceedings{hou2022graphmae,
  title={GraphMAE: Self-Supervised Masked Graph Autoencoders},
  author={Hou, Zhenyu and Liu, Xiao and Cen, Yukuo and Dong, Yuxiao and Yang, Hongxia and Wang, Chunjie and Tang, Jie},
  booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={594--604},
  year={2022}
}