2 citations · 4 across the 9 of their papers we have counts for
10 papers
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…
Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion
Yuan Li, Jun Hu, Jiaxin Jiang +2
Multimodal data plays a critical role in web-based recommendation systems, where information from diverse modalities such as vision and text enhances representation learning. Howev…
EvoClinician: A Self-Evolving Agent for Multi-Turn Medical Diagnosis via Test-Time Evolutionary Learning
Yufei He, Juncheng Liu, Zhiyuan Hu +9
Prevailing medical AI operates on an unrealistic ''one-shot'' model, diagnosing from a complete patient file. However, real-world diagnosis is an iterative inquiry where Clinicians…
Autonomous Chain-of-Thought Distillation for Graph-Based Fraud Detection
Yuan Li, Jun Hu, Bryan Hooi +2
Graph-based fraud detection on text-attributed graphs (TAGs) requires jointly modeling rich textual semantics and relational dependencies. However, existing LLM-enhanced GNN approa…
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)…
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…
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation
Jun Hu, Bryan Hooi, Bingsheng He +1
Multimodal recommendation systems can learn users' preferences from existing user-item interactions as well as the semantics of multimodal data associated with items. Many existing…