111 citations · 176 across the 13 of their papers we have counts for
4 papers · 1 filter
GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks
Bang Wu, He Zhang, Xiangwen Yang +4
The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse…
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Yizhen Zheng, He Zhang, Vincent CS Lee +3
Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophily-prone or heterophily-prone. While graphs with homophily-pron…
A Survey on Fairness-aware Recommender Systems
Di Jin, Luzhi Wang, He Zhang +4
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, whic…
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
He Zhang, Xingliang Yuan, Shirui Pan
Graph neural networks (GNNs) have gained significant attraction due to their expansive real-world applications. To build trustworthy GNNs, two aspects - fairness and privacy - have…