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…
Generative Editing in the Joint Vision-Language Space for Zero-Shot Composed Image Retrieval
Xin Wang, Haipeng Zhang, Mang Li +4
Composed Image Retrieval (CIR) enables fine-grained visual search by combining a reference image with a textual modification. While supervised CIR methods achieve high accuracy, th…
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…
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 (…