A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
| Autor: | Li, Jintang, Zhang, Huizhe, Wu, Ruofan, Zhu, Zulun, Wang, Baokun, Meng, Changhua, Zheng, Zibin, Chen, Liang |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted to ICLR 2024; Code is available at https://github.com/EdisonLeeeee/SpikeGCL |
| Databáze: | arXiv |
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