Fair Network Communities through Group Modularity
Track: Social networks and social media
Keywords: algorithmic fairness, community detection, clustering, social networks, group modularity
TL;DR: We propose a new metric for fairness in communities called group modularity and present fairness-aware community detection algorithms.
Abstract: Communities in networks are groups of nodes that are more densely connected to each other than to the rest of the network, forming clusters with strong internal relationships. When nodes have sensitive attributes, such as demographic groups in social networks, a key question is whether nodes in each group are equally well-connected within each community. We model connectivity fairness through group modularity, an adaptation of modularity that accounts for group structures. We introduce two versions of group modularity grounded on different null models and present fairness-aware community detection algorithms. Finally, we provide experimental results on real and synthetic networks, evaluating both the group modularity of community structure in networks and our fairness-aware algorithms.
Submission Number: 1530
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