A Graph Is Worth 1bit Spikes When Graph Contrastive Learning Meets Spiking Neural Networks

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

**arXiv ID:** 2305.19306
**Authors:** Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Baokun Wang, Changhua Meng, Zibin Zheng, Liang Chen
**Published:** 2023-05-30T16:03:11Z
**Abstract:**
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications. This work explores a promising direction for graph contrastive learning (GCL) with spiking neural networks (SNNs), which leverage sparse and binary characteristics to learn more biologically plausible and compact representations. We propose SpikeGCL, a novel GCL framework to learn binarized 1-bit representations for graphs, making balanced trade-offs between efficiency and performance. We provide theoretical guarantees to demonstrate that SpikeGCL has comparable expressiveness with its full-precision counterparts. Experimental results demonstrate that, with nearly 32x representation storage compression, SpikeGCL is either comparable to or outperforms many fancy state-of-the-art supervised and self-supervised methods across several graph benchmarks.

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This skill is generated from the arXiv paper: A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks (2305.19306).

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## References

- [arXiv:2305.19306](http://arxiv.org/abs/2305.19306v2)