2.7k citations · 3.9k across the 41 of their papers we have counts for
17 papers · 1 filter
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks
Jiaxi Li, Guansong Pang, Ling Chen +1
This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities i…
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
Hongzuo Xu, Yijie Wang, Guansong Pang +3
Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from…
Graph-level Anomaly Detection via Hierarchical Memory Networks
Chaoxi Niu, Guansong Pang, Ling Chen
Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is…
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang +8
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on l…
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection
Zhong Zhuang, Kai Ming Ting, Guansong Pang +1
Graph anomaly detection has attracted a lot of interest recently. Despite their successes, existing detectors have at least two of the three weaknesses: (a) high computational cost…
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning
Chaoxi Niu, Guansong Pang, Ling Chen
Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL meth…
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled…
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Hongzuo Xu, Yijie Wang, Songlei Jian +3
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality de…
Deep Isolation Forest for Anomaly Detection
Hongzuo Xu, Guansong Pang, Yijie Wang +1
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong s…
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities
Zhiyue Wu, Hongzuo Xu, Guansong Pang +4
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been d…
Homophily Outlier Detection in Non-IID Categorical Data
Guansong Pang, Longbing Cao, Ling Chen
Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Indepen…
Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification
Guansong Pang, Ngoc Thien Anh Pham, Emma Baker +2
A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression curren…
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
Guansong Pang, Anton van den Hengel, Chunhua Shen +1
We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important…
Deep Learning for Anomaly Detection: A Review
Guansong Pang, Chunhua Shen, Longbing Cao +1
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still s…
Deep Anomaly Detection with Deviation Networks
Guansong Pang, Chunhua Shen, Anton van den Hengel
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing de…
Deep Weakly-supervised Anomaly Detection
Guansong Pang, Chunhua Shen, Huidong Jin +1
Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved per…
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Guansong Pang, Longbing Cao, Ling Chen +1
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods t…