SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning

arXiv:2404.07941v1 Announce Type: cross
Abstract: In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal dynamics and low-power characteristic, offer an efficient solution for temporal processing in DGRL task. However, owing to the spike-based information encoding mechanism of SNNs, existing DGRL methods employed SNNs face limitations in their representational capacity. Given this issue, we propose a novel framework named Spike-induced …

abstract arxiv cs.ai cs.lg cs.ne domain dynamic dynamics evolution graph graph neural network graph representation low network networks neural network neural networks power processing representation representation learning solution spiking neural networks temporal type world