6 citations · 8 across the 4 of their papers we have counts for
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cs.LG2023★ 6 cited
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
Zemin Liu, Yuan Li, Nan Chen +3
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound…
cs.LG2023
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Yiqun Diao, Yutong Yang, Qinbin Li +2
How to get insights from relational data streams in a timely manner is a hot research topic. Data streams can present unique challenges, such as distribution drifts, outliers, emer…
cs.LG2023★ 2 cited
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
Zhaomin Wu, Junyi Hou, Bingsheng He
Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few pub…