A0486
Title: Minority representation in network rankings: Methods for estimation, testing, and fairness
Authors: Peter MacDonald - University of Waterloo (Canada) [presenting]
Eric Kolaczyk - McGill University (Canada)
Hui Shen - McGill University (Canada)
Abstract: Networks, composed of nodes and their connections, are widely used to model complex relationships across various fields. Centrality metrics often inform decisions such as identifying key nodes or prioritizing resources. However, networks frequently suffer from missing or incorrect edges, which can systematically affect centrality-based decisions and distort the representation of certain protected groups. To address this issue, a formal definition of minority representation is introduced, measured as the proportion of minority nodes among the top-ranked nodes. Systematic bias is modeled against minority groups by using group-dependent missing edge errors. Methods are proposed to estimate and detect systematic bias. Asymptotic limits of minority representation statistics are derived under canonical network models and used to correct the representation of minority groups in node rankings. Simulation results demonstrate the effectiveness of the estimation, testing, and ranking correction procedures, and the methods are applied to a contact network, showcasing their practical applicability.
