Improved Community Detection using Stochastic Block Models

Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block model (SBM) is one of several approaches used to uncover community structure in graphs. In this study, we demonstrate that...

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Bibliographic Details
Main Authors Park, Minhyuk, Feng, Daniel Wang, Digra, Siya, Vu-Le, The-Anh, Chacko, George, Warnow, Tandy
Format Journal Article
LanguageEnglish
Published 19.08.2024
Subjects
Computer Science - Social and Information Networks
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Summary:Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block model (SBM) is one of several approaches used to uncover community structure in graphs. In this study, we demonstrate that SBM software applied to various real-world and synthetic networks produces poorly-connected to disconnected clusters. We present simple modifications to improve the connectivity of SBM clusters, and show that the modifications improve accuracy using simulated networks.
DOI:10.48550/arxiv.2408.10464