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Talk abstract: We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subpopulations of nodes may not share or receive information equitably. We thus propose an optimization-based approach to accurately infer networks while discouraging biased edges. To this end, we present bias metrics that measure topological demographic parity to be applied as convex penalties, suitable for most optimization-based graph learning methods. We also propose an efficient proximal gradient algorithm to obtain the estimates. Theoretically, we express the tradeoff between fair and accurate estimated networks. Critically, this includes demonstrating when accuracy can be preserved in the presence of a fairness regularizer. Our empirical validation includes synthetic and real-world simulations that illustrate the value and effectiveness of our proposed optimization problem and iterative algorithm.
Short bio: Madeline Navarro is a Ph.D. student at Rice University advised by Santiago Segarra. Before joining Rice in 2020, she received the B.Sc. degree in electrical engineering from Old Dominion University in 2019 and the M.Sc. degree in electrical engineering from Rice in 2023. Her research interests include graph signal processing, graphical models, machine learning, optimization, and fairness in data science.