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新的GNN方法通过序列建模增强图学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了新的方法，通过整合现代序列建模的原理来改进图学习。一种方法MP-SSM将状态空间模型（SSM）计算直接嵌入消息传递神经网络框架中，能够对静态图和时间图进行高效且排列等变的远程信息传播。另一种方法SiST-GNN在单一消息传递操作中融合了空间和时间信号，用于动态图表示学习，在链接预测和节点分类任务上取得了显著的最新进展。 AI

影响 图表示学习的这些进展可能导致更复杂的AI模型，用于分析社交网络、推荐系统和科学模拟等领域的复杂动态数据集。

排序理由 两篇介绍图神经网络新方法的论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的GNN方法通过序列建模增强图学习

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两篇介绍图神经网络新方法的论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Andrea Ceni, Alessio Gravina, Claudio Gallicchio, Davide Bacciu, Carola-Bibiane Schonlieb, Moshe Eliasof ·

    消息传递状态空间模型：用现代序列建模改进图学习

    arXiv:2505.18728v2 Announce Type: replace-cross Abstract: The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM module…

  2. arXiv cs.AI TIER_1 English(EN) · Shubhajit Roy, Anirban Dasgupta ·

    动态图表示学习的“Si”多“S”时空消息传递

    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 aggregatio…

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