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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:
- 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
Requirements
- Python >= 3.9.5
- PyTorch >= 1.11.0
- dgl >= 1.0.0
- scikit-learn >= 1.0.2
- PyYAML
- ogb
- tqdm
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}
}