197 citations · 279 across the 11 of their papers we have counts for
5 papers · 1 filter
Towards a Unified Framework of Clustering-based Anomaly Detection
Zeyu Fang, Ming Gu, Sheng Zhou +4
Unsupervised Anomaly Detection (UAD) plays a crucial role in identifying abnormal patterns within data without labeled examples, holding significant practical implications across v…
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Jinke Shi +3
Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real…
Homophily-enhanced Structure Learning for Graph Clustering
Ming Gu, Gaoming Yang, Sheng Zhou +5
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existi…
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Yuanchen Bei, Hao Xu, Sheng Zhou +5
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the adva…
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Sheng Zhou, Hongjia Xu, Zhuonan Zheng +7
Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as fea…