Rethinking Graph Masked Autoencoders through Alignment and Uniformity

Implementation for AAAI'24 paper: Rethinking Graph Masked Autoencoders through Alignment and Uniformity.

Dependencies

• Python >= 3.9.5
• PyTorch >= 1.11.0
• dgl >= 1.0.0
• scikit-learn >= 1.0.2
• PyYAML
• ogb
• tqdm

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 --seed 0 --device 0 --use_cfg
# for inductive node classification
python main_inductive.py --dataset ppi --seed 0 --device 0 --use_cfg

Supported datasets:

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

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  --seed 0 --device 0 --use_cfg

Supported datasets:

• IMDB-BINARY, IMDB-MULTI, PROTEINS, MUTAG, COLLAB,PTC-MR,REDDIT-BINERY

Citing

@article{wang2024augmae,
  author       = {Liang Wang and Xiang Tao and Qiang Liu and Shu Wu and Liang Wang},
  title        = {Rethinking Graph Masked Autoencoders through Alignment and Uniformity},
  journal      = {arXiv},
  volume       = {abs/2402.07225},
  year         = {2024}
}