Improved Community Detection using Stochastic Block Models

TL;DR

This paper improves community detection in networks by modifying stochastic block models to produce better-connected clusters, validated through tests on real-world and synthetic data.

Contribution

The paper introduces simple modifications to SBM that enhance cluster connectivity and accuracy in community detection tasks.

Findings

Modified SBM produces more connected communities

Improvements validated on synthetic networks

Enhanced accuracy over standard SBM

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.