Balanced Stochastic Block Model for Community Detection in Signed Networks

2026-02-16

AI Summary

This work addresses the challenge of community detection in signed networks, where both positive and negative edges jointly shape community structure. The authors propose a novel stochastic block model that integrates structural balance theory, explicitly embedding balance rules—such as “the enemy of my enemy is my friend”—into the generative mechanism to favor the formation of balanced triangles. To enable efficient inference, they develop a profile pseudo-likelihood estimation algorithm and establish strong consistency of community recovery under weak signal conditions. Both theoretical analysis and empirical evaluations demonstrate that the proposed method significantly outperforms conventional approaches that rely solely on connection topology, achieving superior performance on synthetic benchmarks and two real-world signed networks while maintaining robustness and effectiveness.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Community detection, discovering the underlying communities within a network from observed connections, is a fundamental problem in network analysis, yet it remains underexplored for signed networks. In signed networks, both edge connection patterns and edge signs are informative, and structural balance theory (e.g., triangles aligned with ``the enemy of my enemy is my friend''and ``the friend of my friend is my friend''are more prevalent) provides a global higher-order principle that guides community formation. We propose a Balanced Stochastic Block Model (BSBM), which incorporates balance theory into the network generating process such that balanced triangles are more likely to occur. We develop a fast profile pseudo-likelihood estimation algorithm with provable convergence and establish that our estimator achieves strong consistency under weaker signal conditions than methods for the binary SBM that rely solely on edge connectivity. Extensive simulation studies and two real-world signed networks demonstrate strong empirical performance.

Problem

• community detection
• signed networks
• structural balance
• stochastic block model
• network analysis

Innovation

• Balanced Stochastic Block Model
• signed networks
• structural balance theory
• profile pseudo-likelihood
• community detection