Despite the recent interest in fairness-aware community detection, existing methods only focus on fairness along a single demographic, failing to account for multiple demographic attributes and their intersections. This work investigates intersectional fairness in social network community detection, highlighting the impact of demographic distribution and algorithm choice on fairness outcomes. Our findings emphasize the need for community detection methods considering intersectionality and demographic proportionality in order to mitigate biases in social network analysis.

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