32 citations · 49 across the 9 of their papers we have counts for
3 papers · 1 filter
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
Donald Loveland, Jiong Zhu, Mark Heimann +3
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…
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning
Ioannis Tsaknakis, Bhavya Kailkhura, Sijia Liu +4
Using deep learning (DL) to accelerate and/or improve scientific workflows can yield discoveries that are otherwise impossible. Unfortunately, DL models have yielded limited succes…
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing
Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena +1
Graph Neural Networks (GNNs) have proven to excel in predictive modeling tasks where the underlying data is a graph. However, as GNNs are extensively used in human-centered applica…