Improved Community Detection using Stochastic Block Models

2024/08/20 by Park, Minhyuk, Feng, Daniel Wang, Digra, Siya +3 · 2 citations
#FOS: Computer and information sciences #Social and Information Networks (cs.SI)

paper · doi:10.48550/arxiv.2408.10464

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.

Cited by

• Dense Subgraph Clustering and a New Cluster Ensemble Method
• On the Optimization of Methods for Establishing Well-Connected Communities

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