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News/'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning
arxiv
PublishedMay 26, 2026 at 4:00 AM
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'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning

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Publisher summary· verbatim

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{Spa

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    SiST-GNN
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#machine-learning#graph-neural-networks#link-prediction#node-classification

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Mentioned models
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  • 01
    SiST-GNN
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arxiv
Read original ↗All from arxiv →
Tags
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#machine-learning#graph-neural-networks#link-prediction#node-classification

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