221 citations · 231 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 10 cited
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Yu Zheng, Ming Jin, Yixin Liu +3
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detectio…
cs.LG2021★ 221 cited
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
Yu Zheng, Ming Jin, Yixin Liu +3
Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Ex…