SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning
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 perform spatial aggregation, whereas \emph{Spatial-first} approaches invert this order, feeding the output of a graph convolution into a downstream temporal module. In either case, the rigid sequencing forces the second stage to consume an already-compressed summary produced by the first, ruling out joint reasoning over topology and evolution; effectively, the message-passing operator never gets to weight a neighbor's contribution by that neighbor's \emph{past} trajectory. This paper intro...
| Authors | Shubhajit Roy, Anirban Dasgupta |
|---|---|
| Primary category | cs.LG |
| Published | 25 May 2026 |
| Last updated at source | 21 September 2026 |
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