Improved Community Detection using Stochastic Block Models
Social and Information Networks
2025-02-17 v2
Abstract
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.
Cite
@article{arxiv.2408.10464,
title = {Improved Community Detection using Stochastic Block Models},
author = {Minhyuk Park and Daniel Wang Feng and Siya Digra and The-Anh Vu-Le and George Chacko and Tandy Warnow},
journal= {arXiv preprint arXiv:2408.10464},
year = {2025}
}
Comments
See arXiv:2502.00686 for an extended version of this manuscript submitted for review