2 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation
Cecelia Soh, Jun Hu, Yuan Li +6
Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce human-readable explanations fo…
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…
Echoless Label-Based Pre-computation for Memory-Efficient Heterogeneous Graph Learning
Jun Hu, Shangheng Chen, Yufei He +3
Heterogeneous Graph Neural Networks (HGNNs) are widely used for deep learning on heterogeneous graphs. Typical end-to-end HGNNs require repetitive message passing during training,…
DGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs
Yuan Li, Jun Hu, Bryan Hooi +2
Real-world fraud detection applications benefit from graph learning techniques that jointly exploit node features, often rich in textual data, and graph structural information. Rec…
NTSFormer: A Self-Teaching Graph Transformer for Multimodal Isolated Cold-Start Node Classification
Jun Hu, Yufei He, Yuan Li +2
Isolated cold-start node classification on multimodal graphs is challenging because such nodes have no edges and often have missing modalities (e.g., absent text or image features)…