4 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…
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…
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…
Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance
Yufei He, Ruoyu Li, Alex Chen +8
Large language model (LLM) agents often struggle in environments where rules and required domain knowledge frequently change, such as regulatory compliance and user risk screening.…