Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However, current work typically models graph dynamics with recurrent neural networks (RNNs), making them suffer seriously from computation and memory overheads on large temporal graphs. So far, scalability of dynamic graph representation learning on large temporal graphs remains one of the major challenges. In this paper, we present a scalable framework, namely SpikeNet, to efficiently capture the temporal and structural patterns of temporal graphs. We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs. As a low-power alternative to RNNs, SNNs explicitly model graph dynamics as spike trains of neuron populations and enable spike-based propagation in an efficient way. Experiments on three large real-world temporal graph datasets demonstrate that SpikeNet outperforms strong baselines on the temporal node classification task with lower computational costs. Particularly, SpikeNet generalizes to a large temporal graph (2.7M nodes and 13.9M edges) with significantly fewer parameters and computation overheads.Our code is publicly available at \url{https://github.com/EdisonLeeeee/SpikeNet}.

翻译：近年来，动态图表示学习的研究蓬勃发展，其目标是建模随时间动态演化的时序图。然而，现有方法通常使用循环神经网络（RNN）建模图动态性，导致在大规模时序图上存在严重的计算和内存开销问题。目前，动态图表示学习在大规模时序图上的可扩展性仍是主要挑战之一。本文提出了一种可扩展框架SpikeNet，能够高效捕获时序图的时序和结构模式。我们探索了一个新方向：利用脉冲神经网络（SNN）替代RNN来捕获时序图的演化动态性。作为RNN的低功耗替代方案，SNN将图动态性显式建模为神经元群体的脉冲序列，并以高效方式实现基于脉冲的传播。在三个大规模真实时序图数据集上的实验表明，SpikeNet以更低的计算成本在时序节点分类任务上超越了强基线方法。特别地，SpikeNet能够以显著更少的参数和计算开销泛化到大规模时序图（270万节点、1390万边）。我们的代码已开源在\url{https://github.com/EdisonLeeeee/SpikeNet}。