Improved Community Detection using Stochastic Block Models

2024-08-20

arXiv.org

AI Summary

Stochastic Block Models (SBMs) often yield poorly connected or even disconnected clusters in community detection, undermining structural coherence and interpretability. This paper is the first to systematically identify and characterize this structural connectivity deficiency in practical SBM-based clustering. To address it, we propose a lightweight, interpretable structural correction method: a low-overhead edge augmentation mechanism that enhances intra-cluster edge density and global connectivity without increasing model complexity. Our approach integrates SBM graph modeling, community optimization, and quantitative connectivity assessment. Extensive evaluation on diverse synthetic network benchmarks demonstrates that the corrected SBM achieves average improvements of 12.7% in modularity and intra-cluster connectivity rate, while reducing the proportion of disconnected clusters by over 90%. This work advances the structural soundness and reliability of SBM-based community detection, offering both conceptual insight and a practical, deployable tool for real-world applications.

Technology Category

Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityMachine Learning: Graph-based Machine LearningConstraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsWeb Mining and Content Analysis: Models for Web evolution

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.

Problem

• Complex Networks
• Community Detection
• Stochastic Block Model

Innovation

• Well-Connected Clusters (WCC)
• Random Block Model
• High Accuracy and Efficiency