Group Fairness Metrics for Community Detection Methods in Social Networks

TL;DR

This paper introduces group fairness metrics for community detection in social networks, analyzing how different methods balance performance and fairness, especially regarding minority groups, and providing insights for developing equitable algorithms.

Contribution

It proposes new fairness metrics for community detection and compares existing methods to understand their performance-fairness trade-offs.

Findings

Trade-offs vary significantly across methods.

No method consistently outperforms others in fairness.

Metrics help evaluate and develop fair community detection algorithms.

Abstract

Understanding community structure has played an essential role in explaining network evolution, as nodes join communities which connect further to form large-scale complex networks. In real-world networks, nodes are often organized into communities based on ethnicity, gender, race, or wealth, leading to structural biases and inequalities. Community detection (CD) methods use network structure and nodes' attributes to identify communities, and can produce biased outcomes if they fail to account for structural inequalities, especially affecting minority groups. In this work, we propose group fairness metrics ($\Phi^{F*}_{p}$) to evaluate CD methods from a fairness perspective. We also conduct a comparative analysis of existing CD methods, focusing on the performance-fairness trade-off, to determine whether certain methods favor specific types of communities based on their size, density, or conductance. Our findings reveal that the trade-off varies significantly across methods, with no specific type of method consistently outperforming others. The proposed metrics and insights will help develop and evaluate fair and high performing CD methods.