4 papers
Learning Dynamic Graph Representations through Timespan View Contrasts
Yiming Xu, Zhen Peng, Bin Shi +2
The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node features and topological prop…
Generalist Graph Anomaly Detection via Prototype-Based Distillation
Yiming Xu, Zihan Chen, Zhen Peng +4
Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector transferable across new graphs,…
One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents
Zhaoxi Zhang, Yitong Duan, Yanzhi Zhang +9
Locating files and functions requiring modification in large software repositories is challenging due to their scale and structural complexity. Existing LLM-based methods typically…
Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective
Yiming Xu, Zhen Peng, Bin Shi +4
The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-base…