Implementation for KDD'22 paper: GraphMAE: Self-Supervised Masked Graph Autoencoders.
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 0Supported 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 0Supported 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 from https://www.dgl.ai/ when running the code.
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