SourceDetMamba：一种面向序列超图中源头检测的图感知状态空间模型
SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs
AI总结：
本文提出SourceDetMamba模型，将超图建模与Mamba状态空间模型结合，通过逆序输入时间快照和图感知状态更新机制，实现序列超图中的谣言源头检测，并在八个数据集上取得优于现有方法的性能。
摘要：
图上的源头检测在识别谣言起源方面已展现出高效性。尽管基于机器学习的方法取得了进展，但许多方法未能捕捉谣言传播的内在动态。在本工作中，我们提出了SourceDetMamba：一种面向序列超图中源头检测的图感知状态空间模型，该模型利用最近取得成功的状态空间模型Mamba——以其卓越的全局建模能力和计算效率著称——来应对这一挑战。具体而言，我们首先采用超图对社交网络中的高阶交互进行建模。随后，在传播过程中生成的时间网络快照按逆序依次输入Mamba，以推断潜在的传播动态。最后，为了使序列模型能够在整合结构信息的同时有效捕捉传播模式，我们提出了一种新颖的图感知状态更新机制，其中每个节点的状态通过时间依赖性和拓扑上下文进行传播和细化。在八个数据集上的广泛评估表明，SourceDetMamba始终优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
