通过构建高阶关系与特征融合注意力，提升谣言源检测精度。
HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion
- 构建用户间动态与静态高阶关系，捕捉群体传播特性
- 自注意力机制自动学习节点特征，有效区分源点与其他节点
- 适用于复杂社交网络中的群体谣言溯源，尤其在多对多互动场景
超图在建模社交网络方面具有优越能力，尤其能捕捉超越成对交互的群体现象，如谣言传播。现有谣言源检测方法主要关注二元交互，难以应对更复杂的关联结构。本文提出一种基于交互关系构建与特征丰富注意力融合的超图谣言源检测方法（HyperDet）。首先，通过交互关系构建模块精准建模用户间的静态拓扑与动态交互；随后，利用特征丰富注意力融合模块，通过自注意力机制自主学习节点特征，并有效区分节点，从而在准确建模高阶关系的基础上学习节点表示。大量实验验证了该方法的有效性，其性能优于当前最先进方法。
原文摘要 · Abstract (English)
Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the complexity of more intricate relational structures. In this study, we present a novel approach for Source Detection in Hypergraphs (HyperDet) via Interactive Relationship Construction and Feature-rich Attention Fusion. Specifically, our methodology employs an Interactive Relationship Construction module to accurately model both the static topology and dynamic interactions among users, followed by the Feature-rich Attention Fusion module, which autonomously learns node features and discriminates between nodes using a self-attention mechanism, thereby effectively learning node representations under the framework of accurately modeled higher-order relationships. Extensive experimental validation confirms the efficacy of our HyperDet approach, showcasing its superiority relative to current state-of-the-art methods.
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