Individual Fairness in Community Detection: Quantitative Measure and Comparative Evaluation

TL;DR

This paper introduces a new measure for individual fairness in community detection, evaluates various algorithms on synthetic and real networks, and highlights the importance of fairness-aware methods in network analysis.

Contribution

It proposes a novel quantitative measure for individual fairness, and provides a comprehensive empirical evaluation of community detection algorithms regarding fairness and quality trade-offs.

Findings

Individual unfairness can occur despite high group fairness or accuracy.

Fairness depends on the detectability of community structure.

Certain algorithms like Significance, Surprise, Combo, Leiden, and SBMDL balance fairness and quality better.

Abstract

Community detection is a fundamental task in complex network analysis. Fairness-aware community detection seeks to prevent biased node partitions, typically framed in terms of individual fairness, which requires similar nodes to be treated similarly, and group fairness, which aims to avoid disadvantaging specific groups of nodes. While existing literature on fair community detection has primarily focused on group fairness, we introduce a novel measure to quantify individual fairness in community detection methods. The proposed measure captures unfairness as the vectorial distance between a node's true and predicted community representations, computed using the community co-occurrence matrix. We provide a comprehensive empirical investigation of a broad set of community detection algorithms from the literature on both synthetic networks, with varying levels of community explicitness, and real-world networks. We particularly investigate the fairness-performance trade-off using standard quality metrics and compare individual fairness outcomes with existing group fairness measures. The results show that individual unfairness can occur even when group fairness or clustering accuracy is high, underscoring that individual and group fairness are not interchangeable. Moreover, fairness depends critically on the detectability of community structure. However, we find that Significance and Surprise for denser graphs, and Combo, Leiden, and SBMDL for sparser graphs result in a better trade-off between individual fairness and community quality. Overall, our findings, together with the fact that community detection is an important step in many network analysis downstream tasks, highlight the necessity of developing fairness-aware community detection methods.