40 citations · 82 across the 11 of their papers we have counts for
11 papers
Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation
Seunghan Lee, Hyunsik Yoo, Jian Kang +2
Multi-behavior recommendation (MBR) leverages auxiliary behavioral signals, such as clicks and add-to-cart, to enhance target behavior prediction like purchases. While recent graph…
PageRank Bandits for Link Prediction
Yikun Ban, Jiaru Zou, Zihao Li +5
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been dire…
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method
Weilin Cong, Jian Kang, Hanghang Tong +1
Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves ov…
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…
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks
Jian Kang, Qinghai Zhou, Hanghang Tong
Graph Convolutional Network (GCN) has exhibited strong empirical performance in many real-world applications. The vast majority of existing works on GCN primarily focus on the accu…
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Jian Kang, Yan Zhu, Yinglong Xia +2
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications. Despite the successes of GCN deployment, GCN often exhibits performance disparity with respec…
InfoFair: Information-Theoretic Intersectional Fairness
Jian Kang, Tiankai Xie, Xintao Wu +2
Algorithmic fairness is becoming increasingly important in data mining and machine learning. Among others, a foundational notation is group fairness. The vast majority of the exist…
AURORA: Auditing PageRank on Large Graphs
Jian Kang, Meijia Wang, Nan Cao +3
Ranking on large-scale graphs plays a fundamental role in many high-impact application domains, ranging from information retrieval, recommender systems, sports team management, bio…