SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning.

arXiv:2605.25548v2 Announce Type: replace-cross Abstract: Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \emph{Temporal-first} approaches build per-node temporal embeddings and only afterward