MOUFLON: Multi-group Modularity-based Fairness-aware Community Detection

2025-10-14

AI Summary

This work addresses the challenge of simultaneously optimizing modularity and group fairness in modular community detection—particularly in real-world networks with multiple, imbalanced groups differing in size, density, and distribution. We propose a novel proportionality-based fairness metric that ensures cross-network comparability, and integrate it into a modularity optimization framework via a tunable, fairness-constrained objective function. Extensive experiments on synthetic and real-world networks demonstrate that our method effectively balances modularity and fairness, maintains robust fairness performance even under high intra-group homophily, and reveals mechanistic insights into how network scale, density, and group distribution influence fairness outcomes. Our core contribution is the first unified modeling of a strictly tunable, multi-group-compatible fairness metric within modularity maximization—providing both theoretical foundations and practical tools for fair community discovery.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Game Theory and Economic Paradigms: Fair Division
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In this paper, we propose MOUFLON, a fairness-aware, modularity-based community detection method that allows adjusting the importance of partition quality over fairness outcomes. MOUFLON uses a novel proportional balance fairness metric, providing consistent and comparable fairness scores across multi-group and imbalanced network settings. We evaluate our method under both synthetic and real network datasets, focusing on performance and the trade-off between modularity and fairness in the resulting communities, along with the impact of network characteristics such as size, density, and group distribution. As structural biases can lead to strong alignment between demographic groups and network structure, we also examine scenarios with highly clustered homogeneous groups, to understand how such structures influence fairness outcomes. Our findings showcase the effects of incorporating fairness constraints into modularity-based community detection, and highlight key considerations for designing and benchmarking fairness-aware social network analysis methods.

Problem

• Balancing partition quality with fairness in community detection
• Providing consistent fairness metrics for multi-group networks
• Analyzing fairness-modularity trade-offs across diverse network structures

Innovation

• Proportional balance fairness metric for multi-group settings
• Adjustable trade-off between modularity and fairness outcomes
• Handles imbalanced networks with clustered homogeneous groups