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

2025/05/19 by Le Cheng, Cheng, Le, Peican Zhu +9
Computer Science · Physics and Astronomy · Social Sciences · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #FOS: Computer and information sciences #Misinformation and Its Impacts #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2505.12910

openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• PointMamba: A Simple State Space Model for Point Cloud Analysis
• Graph Mamba: Towards Learning on Graphs with State Space Models
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• HGNN + : General Hypergraph Neural Networks
• An Invertible Graph Diffusion Neural Network for Source Localization
• Efficiently Modeling Long Sequences with Structured State Spaces
• Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
• HiPPO: Recurrent Memory with Optimal Polynomial Projections
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
• Networks beyond pairwise interactions: Structure and dynamics
• Hypergraph Convolution and Hypergraph Attention
• Hypergraph convolution and hypergraph attention
• Hypergraph Neural Networks
• Catch'Em All: Locating Multiple Diffusion Sources in Networks with Partial Observations
• Semi-Supervised Classification with Graph Convolutional Networks
• Locating the Source of Diffusion in Large-Scale Networks
• Rumors in a Network: Who's the Culprit?

Related

• CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality
• Rumor propagation on hypergraphs
• HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion
• Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator
• Graph Neural Networks for Source Detection: A Review and Benchmark Study