77 citations · 175 across the 14 of their papers we have counts for
6 papers · 1 filter
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
Yushun Dong, Song Wang, Jing Ma +2
Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially ren…
CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra +2
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted lab…
Learning Causal Effects on Hypergraphs
Jing Ma, Mengting Wan, Longqi Yang +3
Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existi…
Empowering Next POI Recommendation with Multi-Relational Modeling
Zheng Huang, Jing Ma, Yushun Dong +2
With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences…
Fairness in Graph Mining: A Survey
Yushun Dong, Jing Ma, Song Wang +2
Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most o…
Learning Fair Node Representations with Graph Counterfactual Fairness
Jing Ma, Ruocheng Guo, Mengting Wan +3
Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existin…