TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation. (arXiv:2210.01171v2 [cs.LG] UPDATED)

Temporal graph is an abstraction for modeling dynamic systems that consist of
evolving interaction elements. In this paper, we aim to solve an important yet
neglected problem -- how to learn information from high-order neighbors in
temporal graphs? -- to enhance the informativeness and discriminativeness for
the learned node representations. We argue that when learning high-order
information from temporal graphs, we encounter two challenges, i.e.,
computational inefficiency and over-smoothing, that cannot be solved by
conventional techniques applied on static graphs. …

abstraction aim arxiv challenges computational dynamic graph graphs how to learn information learn modeling neighbors node paper propagation systems temporal