Proceedings

SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs

Le Cheng, Peican Zhu, Yangming Guo, Chao Gao, Zhen Wang, Keke Tang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 2749-2757.

https://doi.org/10.24963/ijcai.2025/306

Keywords

  • Machine Learning: ML: Deep learning architectures
  • Machine Learning: ML: Convolutional networks
  • Data Mining: DM: Networks
  • Data Mining: DM: Mining graphs

Abstract

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.

Le Cheng, Peican Zhu, Yangming Guo, Chao Gao, Zhen Wang, and Keke Tang. 2025. SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 2749–2757. https://doi.org/10.24963/ijcai.2025/306
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