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cs.LG2026
Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach
Yunhui Liu, Qizhuo Xie, Yinfeng Chen +4
Graph anomaly detection (GAD) aims to identify nodes that deviate from normal patterns in structure or features. While recent GNN-based approaches have advanced this task, they str…
cs.LG2025
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection
Yunhui Liu, Jiashun Cheng, Yiqing Lin +7
Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1) label scarcity stemming from the high cost of a…
cs.LG2024
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
Yunhui Liu, Qizhuo Xie, Jinwei Shi +2
Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespre…