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

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}
}