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Disparate access to resources by different subpopulations is a prevalentissue in societal and sociotechnical networks. For example, urbaninfrastructure networks may enable certain racial groups to more easily accessresources such as high-quality schools, grocery stores, and polling places.Similarly, social networks within universities and organizations may enablecertain groups to more easily access people with valuable information orinfluence. Here we introduce a new class of problems, Graph Augmentation forEquitable Access (GAEA), to enhance equity in networked systems by editinggraph edges under budget constraints. We prove such problems are NP-hard, andcannot be approximated within a factor of $(1-\tfrac{1}{3e})$. We develop aprincipled, sample- and time- efficient Markov Reward Process (MRP)-basedmechanism design framework for GAEA. Our algorithm outperforms baselines on adiverse set of synthetic graphs. We further demonstrate the method onreal-world networks, by merging public census, school, and transportationdatasets for the city of Chicago and applying our algorithm to findhuman-interpretable edits to the bus network that enhance equitable access tohigh-quality schools across racial groups. Further experiments on Facebooknetworks of universities yield sets of new social connections that wouldincrease equitable access to certain attributed nodes across gender groups.

GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning