State Space Models on Temporal Graphs: A First-Principles Study

arXiv:2406.00943v1 Announce Type: new
Abstract: 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 …

abstract arxiv complex systems cs.ai cs.lg dynamic graph graph learning graphs practice research snapshots space state state space models study systems temporal type world