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 Graph Neural Network (SiGNN) for learning enhanced
spatialtemporal representations on dynamic graphs. In detail, a harmonious
integration of SNNs and GNNs is achieved through an innovative Temporal
Activation (TA) mechanism. Benefiting from the TA mechanism, SiGNN not only
effectively exploits the temporal dynamics of SNNs but also adeptly circumvents
the representational constraints imposed by the binary nature of spikes.
Furthermore, leveraging the inherent adaptability of SNNs, we explore an
in-depth analysis of the evolutionary patterns within dynamic graphs across
multiple time granularities. This approach facilitates the acquisition of a
multiscale temporal node representation.Extensive experiments on various
real-world dynamic graph datasets demonstrate the superior performance of SiGNN
in the node classification task.

通过融合 SNN 和 GNN 并应用新颖的 Temporal Activation 机制，该研究提出了一种名为 SiGNN 的框架，用于在动态图上学习增强的时空表示，通过在多个时间粒度上深入分析动态图中的演化模式实现多尺度时空节点表示。实验证明 SiGNN 在节点分类任务中具有出色的性能。