7 citations · 11 across the 6 of their papers we have counts for
6 papers
NodeImport: Imbalanced Node Classification with Node Importance Assessment
Nan Chen, Zemin Liu, Bryan Hooi +3
In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performa…
Multi-Label Node Classification with Label Influence Propagation
Yifei Sun, Zemin Liu, Bryan Hooi +4
Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI…
RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
Yuan Li, Jun Hu, Jiaxin Jiang +3
Recent advances in graph learning have paved the way for innovative retrieval-augmented generation (RAG) systems that leverage the inherent relational structures in graph data. How…
Partitioning Message Passing for Graph Fraud Detection
Wei Zhuo, Zemin Liu, Bryan Hooi +4
Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing…
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Qian Wang, Zemin Liu, Zhen Zhang +1
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs). To address this chall…
EX-Graph: A Pioneering Dataset Bridging Ethereum and X
Qian Wang, Zhen Zhang, Zemin Liu +3
While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant…