6 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Spade: A Real-Time Fraud Detection Framework on Evolving Graphs (Complete Version)
Jiaxin Jiang, Yuan Li, Bingsheng He +3
Real-time fraud detection is a challenge for most financial and electronic commercial platforms. To identify fraudulent communities, Grab, one of the largest technology companies i…