Graph anomaly detection has gained significant attention across various domains, particularly in critical applications like fraud detection in e-commerce platforms and insider threat detection in cybersecurity. Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity. However, in the era of big data, the heterogeneity of views and the lack of label information pose substantial challenges to traditional approaches. Existing unsupervised graph anomaly detection methods often struggle with high-dimensionality issues, rely on strong assumptions about graph structures or fail to handle complex multi-view graphs. To address these challenges, we propose a cluster aware multi-view graph anomaly detection method, called CARE. Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label (i.e., soft membership assignments) without any strong assumption about the graph. To mitigate potential biases from the pseudo-label, we introduce a similarity-guided loss. Theoretically, we show that the proposed similarity-guided loss is a variant of contrastive learning loss, and we present how this loss alleviates the bias introduced by pseudo-label with the connection to graph spectral clustering. Experimental results on several datasets demonstrate the effectiveness and efficiency of our proposed framework. Specifically, CARE outperforms the second-best competitors by more than 39% on the Amazon dataset with respect to AUPRC and 18.7% on the YelpChi dataset with respect to AUROC. The code of our method is available at the GitHub link: https://github.com/zhenglecheng/CARE-demo.
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；HKUST(GZ)（香港科技大学（广州））；Tsinghua University（清华大学）；The University of Queensland（昆士兰大学）
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
Telenor Research and Innovation（Telenor研究与创新）；Norwegian Research Council（挪威研究理事会）；Telenor Denmark OSS & Tech Analytics Denmark（Telenor丹麦OSS与技术分析丹麦）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；SINTEF Digital（SINTEF数字技术）；Hance
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness
National University of Defense Technology（国防科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Intelligent Game and Decision Laboratory（智能博弈与决策实验室）
Fairness-aware Anomaly Detection via Fair Projection
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人社会研究院）；The Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来网络智能化重点实验室）
Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative
The University of Queensland（昆士兰大学）；Australian e-Health Research Centre（澳大利亚电子健康研究中心）；Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy（创新医学工程系，威尼斯大学，意大利）；School of Computer Science and Engineering, Beihang University, China（计算机科学与工程学院，北航大学，中国）；Dept. of Computer Science, Reykjavik University, Iceland（计算机科学系，雷克雅未克大学，冰岛）
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
Telenor Research and Innovation（Telenor研究与创新）；Telenor Denmark OSS & Tech Analytics（Telenor丹麦OSS与技术分析）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；SINTEF Digital（SINTEF数字）；Hance
ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring
Department of Computer Science, Gran Sasso Science Institute（计算机科学系，格兰萨索科学研究所）；University of Bergen（卑尔根大学）；Department of Computer Science, Toronto Metropolitan University（计算机科学系，多伦多 Metropolitan 大学）；Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences（计算机科学、电气工程和数学科学系，西挪威应用科学大学）
Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection
School of Engineering and Built Environment, Griffith University（格里菲斯大学工程与环境学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Statistical Inference for Clustering-based Anomaly Detection
University of Information Technology, Ho Chi Minh City, Vietnam（越南胡志明市信息技术大学）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学）；RIKEN, Japan（日本理化学研究所）
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection
School of Computer Science and Technology, Harbin University of Science and Technology（哈尔滨理工大学计算机科学与技术学院）；Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University（闽江学院福建省信息处理与智能控制重点实验室）
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning
Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）；The Hong Kong Polytechnic University（香港理工大学）；Pazhou Lab（琶洲实验室）
