2 papers
cs.LG2026
Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
Qiuran Zhao, Kai Ming Ting, Xinpeng Li
The task of graph-level anomaly detection (GLAD) is to identify anomalous graphs that deviate significantly from the majority of graphs in a dataset. While deep GLAD methods have s…
cs.LG2026
Rethinking Divisive Hierarchical Clustering from a Distributional Perspective
Kaifeng Zhang, Kai Ming Ting, Tianrun Liang +1
We uncover that current objective-based Divisive Hierarchical Clustering (DHC) methods produce a dendrogram that does not have three desired properties i.e., no unwarranted splitti…