'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning 事件

'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning arXiv:2605.25548v1 Announce Type: 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 afterwards perform spatial aggregation, whereas \emph{Spatial-first} approaches invert this order, feeding the output of a graph convolution into a downstream tem