mlsanityData to 13 Oct 2026

Cluster Aware Graph Anomaly Detection

arXiv 2409.09770 ↗First posted 15 Sep 2024cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 37 of its 99 references that are in the map. First posted on arXiv 15 Sep 2024.

Builds on

37 of 99 references.

References · 37

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25 more
  1. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  2. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 4.

7 papers.

  1. 2024PyG-SSL: A Graph Self-Supervised Learning Toolkitcited by 14
  2. 2024Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?cited by 13
  3. 2026OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detectioncited by 2
  4. 2024Online Multi-modal Root Cause Identification in Microservice Systemscited by 1
  5. 2026AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detectioncited by 1
  6. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 1
  7. 2026TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detectioncited by 0