GraphMAE: Self-Supervised Masked Graph Autoencoders

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.

❗ Update

[2023-04-12] GraphMAE2 is published and the code can be found here.

[2022-12-14] The PYG implementation of GraphMAE for node / graph classification is available at this branch.

Dependencies

• Python >= 3.7
• Pytorch >= 1.9.0
• dgl >= 0.7.2
• pyyaml == 5.4.1

Quick Start

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

Datasets used in node classification and graph classification will be downloaded automatically from https://www.dgl.ai/ when running the code.

Experimental Results

Node classification (Micro-F1, %):

Graph classification (Accuracy, %)

Transfer learning on molecular property prediction (ROC-AUC, %):

Citing

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}
}