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[ICLR 2024] Official implementation of Spiking Graph Contrastive Learning (0️⃣1️⃣ SpikeGCL)

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0️⃣1️⃣ SpikeGCL (Spiking Graph Contrastive Learning)

A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks

Jintang Li1, Huizhe Zhang1, Ruofan Wu2, Zulun Zhu3, Baokun Wang2, Changhua Meng2, Zibin Zheng1, Liang Chen1

1Sun Yat-sen University, 2Ant Group, 3Nanyang Technological University

arXiv (arXiv:2305.19306), OpenReview (ICLR'24)

Poster | Slides

Environments

Note

Higher versions should be also compatible.

  • numpy == 1.23.3
  • torch == 1.8+cu111
  • torch-cluster == 1.6.1
  • torch_geometric == 2.3.0
  • torch-scatter == 2.1.1
  • torch-sparse == 0.6.17
  • CUDA 11.1
  • cuDNN 8.0.5

Model and Results

SpikeGCL adopts a simple GCL architecture and is comprised of a set of peer GNN encoders and a spiking neuron.

The following tables present the performance & efficiency results for standard node classification tasks on several graph benchmark datasets.

Reproduction

  • Cora
python main.py --dataset Cora --threshold 5e-4 --outs 2 --T 64 --bn --epochs 5
  • Citeseer
python main.py --dataset Citeseer --threshold 5e-3 --T 32 --bn --epochs 5
  • Pubmed
python main.py --dataset Pubmed --threshold 5e-2 --bn --T 32 --epochs 50
  • Computers
python main.py --dataset Computers --threshold 5e-2 --outs 32 --bn --T 25
  • Photo
python main.py --dataset Photo --threshold 5e-2 --T 15 --bn --outs 8 --epochs 50
  • CS
python main.py --dataset CS --threshold 5e-1 --outs 32 --T 60 --dropout 0. --bn
  • Physics
python main.py --dataset Physics --T 25 --outs 16 --margin 1 --threshold 5e-2 --bn
  • Ogbn-arXiv
python main.py --dataset ogbn-arxiv --T 30 --outs 1 --threshold 5e-2 --no_shuffle --bn --dropout 0.
  • Ogbn-MAG
python main.py --dataset ogbn-mag --T 8 --outs 8 --hids 64 --threshold 5e-3 --no_shuffle --bn

Citation

If you find this repository useful in your research, please consider giving a star ⭐ and a citation

@inproceedings{spikegcl,
    title={A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks},
    author={Jintang Li and Huizhe Zhang and Ruofan Wu and Zulun Zhu and Baokun Wang and Changhua Meng and Zibin Zheng and Liang Chen},
    booktitle={ICLR},
    year={2024},
    url={https://openreview.net/forum?id=LnLySuf1vp}
}

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[ICLR 2024] Official implementation of Spiking Graph Contrastive Learning (0️⃣1️⃣ SpikeGCL)

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