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Found 2 papers, 1 papers with code
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
However, the costs behind outstanding performances of GTs are higher energy consumption and computational overhead.
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
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.
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