3 papers
cs.IR2025
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,…
cs.LG2024
Reconstructing Graph Diffusion History from a Single Snapshot
Ruizhong Qiu, Dingsu Wang, Lei Ying +3
Diffusion on graphs is ubiquitous with numerous high-impact applications. In these applications, complete diffusion histories play an essential role in terms of identifying dynamic…
cs.LG2024
Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +7
Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (…