[ICML 2021] A Unified Lottery Tickets Hypothesis for Graph Neural Networks

Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhangyang Wang

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).

Detials are refer to our Paper.

Refer to README

Refer to README

Refer to Ogbn_ArXiv (README)

Refer to Ogbn_Proteins (README)

Refer to Ogbn_Collab (README)

@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}
}

https://github.com/Shen-Lab/SS-GCNs

https://github.com/cmavro/Graph-InfoClust-GIC

https://github.com/lightaime/deep_gcns_torch