On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods

no code implementations • 10 Jul 2022 • Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T. Schaub, Danai Koutra

We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i. e., the tendency of linked nodes to have similar attributes.

Attribute Fairness +1