43 citations · 83 across the 8 of their papers we have counts for
11 papers
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
Donald Loveland, Jiong Zhu, Mark Heimann +3
Graph Neural Network (GNN) research has highlighted a relationship between high homophily (i.e., the tendency of nodes of the same class to connect) and strong predictive performan…
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…
Reflexive Design for Fairness and Other Human Values in Formal Models
Benjamin Fish, Luke Stark
Algorithms and other formal models purportedly incorporating human values like fairness have grown increasingly popular in computer science. In response to sociotechnical challenge…
On the Complexity of Learning from Label Proportions
Benjamin Fish, Lev Reyzin
In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given.…
Gaps in Information Access in Social Networks
Benjamin Fish, Ashkan Bashardoust, danah boyd +3
The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individ…
Sampling Without Compromising Accuracy in Adaptive Data Analysis
Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein
In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms. This is important…