Addressing overlapping communities in multiple-source detection: An edge clustering approach for complex networks

TL;DR

This paper presents an edge clustering approach integrated with community label propagation to effectively detect multiple sources in complex networks, especially with overlapping communities, outperforming existing methods.

Contribution

It introduces a novel edge clustering method within a label propagation framework for multiple-source detection in networks with overlapping communities.

Findings

Achieves higher F1-Measure accuracy than state-of-the-art algorithms.

Demonstrates robustness in networks with overlapping source regions.

Effective in complex, real-world social network datasets.

Abstract

The source detection problem in network analysis involves identifying the origins of diffusion processes, such as disease outbreaks or misinformation propagation. Traditional methods often focus on single sources, whereas real-world scenarios frequently involve multiple sources, complicating detection efforts. This study addresses the multiple-source detection (MSD) problem by integrating edge clustering algorithms into the community-based label propagation framework, effectively handling mixed-membership issues where nodes belong to multiple communities. The proposed approach applies the automated latent space edge clustering model to a network, partitioning infected networks into edge-based clusters to identify multiple sources. Simulation studies on ADD HEALTH social network datasets demonstrate that this method achieves superior accuracy, as measured by the F1-Measure, compared to state-of-the-art clustering algorithms. The results highlight the robustness of edge clustering in accurately detecting sources, particularly in networks with complex and overlapping source regions. This work advances the applicability of clustering-based methods to MSD problems, offering improved accuracy and adaptability for real-world network analyses.