5 citations · 5 across the 4 of their papers we have counts for
5 papers
Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
Chunyu Wei, Siyuan He, Yu Wang +7
Graph anomaly detection (GAD) is crucial in applications like fraud detection and cybersecurity. Despite recent advancements using graph neural networks (GNNs), two major challenge…
T-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual Graphs
Chunyu Wei, Huaiyu Qin, Siyuan He +2
Retrieval-Augmented Generation (RAG) has significantly enhanced Large Language Models' ability to access external knowledge, yet current graph-based RAG approaches face two critica…
GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining
Chunyu Wei, Wenji Hu, Xingjia Hao +5
Large Language Models (LLMs) face significant limitations when applied to large-scale graphs, struggling with context constraints and inflexible reasoning. We present GraphChain, a…
Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting
Senhao Liu, Zhiyu Guo, Zhiyuan Ji +5
Typical financial risk management involves distinct phases for pre-service risk assessment and in-service default detection, often modeled separately. This paper proposes a novel f…
Graph Evidential Learning for Anomaly Detection
Chunyu Wei, Wenji Hu, Xingjia Hao +4
Graph anomaly detection faces significant challenges due to the scarcity of reliable anomaly-labeled datasets, driving the development of unsupervised methods. Graph autoencoders (…