Over the past few years, research on deep graph learning has shifted from
static graphs to temporal graphs in response to real-world complex systems that
exhibit dynamic behaviors. In practice, temporal graphs are formalized as an
ordered sequence of static graph snapshots observed at discrete time points.
Sequence models such as RNNs or Transformers have long been the predominant
backbone networks for modeling such temporal graphs. Yet, despite the promising
results, RNNs struggle with long-range dependencies, while transformers are
burdened by quadratic computational complexity. Recently, state space models
(SSMs), which are framed as discretized representations of an underlying
continuous-time linear dynamical system, have garnered substantial attention
and achieved breakthrough advancements in independent sequence modeling. In
this work, we undertake a principled investigation that extends SSM theory to
temporal graphs by integrating structural information into the online
approximation objective via the adoption of a Laplacian regularization term.
The emergent continuous-time system introduces novel algorithmic challenges,
thereby necessitating our development of GraphSSM, a graph state space model
for modeling the dynamics of temporal graphs. Extensive experimental results
demonstrate the effectiveness of our GraphSSM framework across various temporal
graph benchmarks.

本研究通过将结构信息整合到在线逼近目标中，采用拉普拉斯正则化项，将状态空间模型理论扩展到时间图，提出了 GraphSSM 框架，用于建模时间图的动态性，实验证明 GraphSSM 在各种时间图基准测试中的有效性。