37 citations · 45 across the 14 of their papers we have counts for
10 papers · 1 filter
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
Wei Huang, Hezhe Qiao, Kailai Zhang +3
Unsupervised tabular anomaly detection methods typically learn feature patterns from normal samples during training and subsequently identify samples that deviate from these patter…
TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
Hui He, Hezhe Qiao, Yutong Chen +2
Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstrea…
Normality Calibration in Semi-supervised Graph Anomaly Detection
Guolei Zeng, Hezhe Qiao, Guoguo Ai +2
Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications. Semi-supervised GAD, which assumes a subse…
Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
Guoguo Ai, Hezhe Qiao, Hui Yan +1
Semi-supervised graph anomaly detection (GAD) utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. Current method…
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
Hezhe Qiao, Chaoxi Niu, Ling Chen +1
Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years…
GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
Guoguo Ai, Guansong Pang, Hezhe Qiao +2
Graph Transformers (GTs) have demonstrated remarkable performance in graph representation learning over popular graph neural networks (GNNs). However, self--attention, the core mod…
Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
Chaoxi Niu, Hezhe Qiao, Changlu Chen +2
Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However,…
Deep Graph Anomaly Detection: A Survey and New Perspectives
Hezhe Qiao, Hanghang Tong, Bo An +3
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its si…
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
Yutong Chen, Hongzuo Xu, Guansong Pang +3
Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is…
Generative Semi-supervised Graph Anomaly Detection
Hezhe Qiao, Qingsong Wen, Xiaoli Li +2
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively ex…