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

TL;DR

SourceDetMamba introduces a graph-aware state space model leveraging hypergraphs and a novel state update mechanism to improve rumor source detection in social networks, demonstrating superior performance across multiple datasets.

Contribution

The paper develops SourceDetMamba, a novel model combining hypergraph modeling, reverse temporal processing, and a graph-aware state update for enhanced source detection.

Findings

Outperforms existing methods on eight datasets

Effectively captures high-order interactions and propagation dynamics

Demonstrates superior accuracy and efficiency

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.