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[2112.05128] Fair Community Detection and Structure Learning in Heterogeneous Graphical Models
Summary
This paper presents a novel approach for fair community detection in heterogeneous graphical models, ensuring demographic representation within communities through a statistical framework.
Why It Matters
As community detection algorithms increasingly influence social and organizational structures, ensuring fairness in these models is crucial. This research addresses the potential biases in community representation, promoting equitable outcomes in data-driven decisions.
Key Takeaways
- Introduces a pseudo-likelihood approach for fair community detection.
- Ensures demographic groups are fairly represented in identified communities.
- Proves statistical consistency for Gaussian and Ising models.
- Utilizes convex semidefinite programming for known graph structures.
- Addresses biases in community detection algorithms.
Statistics > Machine Learning arXiv:2112.05128 (stat) [Submitted on 9 Dec 2021 (v1), last revised 20 Feb 2026 (this version, v3)] Title:Fair Community Detection and Structure Learning in Heterogeneous Graphical Models Authors:Davoud Ataee Tarzanagh, Laura Balzano, Alfred O. Hero View a PDF of the paper titled Fair Community Detection and Structure Learning in Heterogeneous Graphical Models, by Davoud Ataee Tarzanagh and 2 other authors View PDF HTML (experimental) Abstract:Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. Certain demographics may be over-represented in some detected communities and under-represented in others. This paper defines a novel $\ell_1$-regularized pseudo-likelihood approach for fair graphical model selection. In particular, we assume there is some community or clustering structure in the true underlying graph, and we seek to learn a sparse undirected graph and its communities from the data such that demographic groups are fairly represented within the communities. In the case when the graph is known a priori, we provide a convex semidefinite programming approach for fair community detection. We establish the statistical consistency of the proposed method for both a Gaussian graphical model and an Ising model for, respectively, continuous and binary data, proving that our method can recover the graphs and their fair communities with high probab...
