  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.
