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Rethinking Graph Masked Autoencoders through Alignment and Uniformity

<img src="imgs/model.jpg" alt="model" style="zoom: 40%;" />

This is the code for the AAAI'24 Paper: Rethinking Graph Masked Autoencoders through Alignment and Uniformity.

Usage

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:

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:

Requirements

Citation

Please cite our paper if you use the code:

@inproceedings{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},
  booktitle    = {AAAI},
  pages        = {15528--15536},
  year         = {2024}
}