40 citations · 82 across the 11 of their papers we have counts for
4 papers · 1 filter
Deceptive Fairness Attacks on Graphs via Meta Learning
Jian Kang, Yinglong Xia, Ross Maciejewski +2
We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively? W…
Ensuring User-side Fairness in Dynamic Recommender Systems
Hyunsik Yoo, Zhichen Zeng, Jian Kang +7
User-side group fairness is crucial for modern recommender systems, aiming to alleviate performance disparities among user groups defined by sensitive attributes like gender, race,…
BeMap: Balanced Message Passing for Fair Graph Neural Network
Xiao Lin, Jian Kang, Weilin Cong +1
Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in introducing or amplify…
Do We Really Need Complicated Model Architectures For Temporal Networks?
Weilin Cong, Si Zhang, Jian Kang +5
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning. Interestingly, we fo…