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A Unified Lottery Tickets Hypothesis for Graph Neural Networks
[ICML 2021] A Unified Lottery Tickets Hypothesis for Graph Neural Networks
Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhangyang Wang
Overview
<img src = "./Figs/Teaser.png" align = "left" width="45%" hight="45%"> Summary of our achieved performance (y-axis) at different graph and GNN sparsity levels (x-axis) on Cora and Citeceer node classification. The size of markers represent the inference MACs (= 0.5 FLOPs) of each sparse GCN on the corresponding sparsified graphs. Black circles indicate the baseline, i.e., unpruned dense GNNs on the full graph. Blue circles are random pruning results. Orange circles represent the performance of a previous graph sparsification approach, i.e., ADMM. Red stars are established by our method (UGS).
<br/>Methodlody
Detials are refer to our Paper.
Implementation
Node classification on Cora, Citeseer, PubMed
Refer to README
Link Prediction on Cora, Citeseer, PubMed
Refer to README
Experiments on OGB Datasets
Refer to Ogbn_ArXiv (README)
Refer to Ogbn_Proteins (README)
Refer to Ogbn_Collab (README)
Citation
@misc{chen2021unified,
title={A Unified Lottery Ticket Hypothesis for Graph Neural Networks},
author={Tianlong Chen and Yongduo Sui and Xuxi Chen and Aston Zhang and Zhangyang Wang},
year={2021},
eprint={2102.06790},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Acknowledgement
https://github.com/Shen-Lab/SS-GCNs