Lanzhen Guo

A Counterfactual Inspired Framework For Quantifying Edge Effects On Gnns Fairness

Graph Neural Networks (GNNs) play a pivotal role in graph representation learning, addressing challenges across diverse applications. Despite their significance, data-driven GNNs often overlook biases, raising fairness concerns. Inspired by counterfactuals, we inquire, ’In graph data, how does remov…