mlsanityData to 14 Oct 2026

Class Label-aware Graph Anomaly Detection

arXiv 2308.11669 ↗First posted 22 Aug 2023cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a node is anomalous or not. One common observation we made from previous unsupervised methods is that they not only assume the absence of such anomaly labels, but also the absence of class labels (the class a node belongs to used in a general node classification task). In this work, we study the utility of class labels for unsupervised GAD; in particular, how they enhance the detection of structural anomalies. To this end, we propose a Class Label-aware Graph Anomaly Detection framework (CLAD) that utilizes a limited amount of labeled nodes to enhance the performance of unsupervised GAD. Extensive experiments on ten datasets demonstrate the superior performance of CLAD in comparison to existing unsupervised GAD methods, even in the absence of ground-truth class label information. The source code for CLAD is available at urlhttps://github.com/jhkim611/CLAD.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 22 Aug 2023.

Builds on

11 of 24 references.

References · 11

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  4. 2015Image-based Recommendations on Styles and Substitutescited by 706
  5. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
  6. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  7. 2014Graph-based Anomaly Detection and Description: A Surveycited by 183
  8. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  9. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
  10. 2020AnomalyDAE: Dual autoencoder for anomaly detection on attributed networkscited by 82
  11. 2022DAGAD: Data Augmentation for Graph Anomaly Detectioncited by 20

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Cited by year

Citing papers per year: 2024: 5; 2025: 2.

7 papers.

  1. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  2. 2024SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detectioncited by 11
  3. 2024Self-Guided Robust Graph Structure Refinementcited by 8
  4. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  5. 2024Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earlinesscited by 3
  6. 2025Training Robust Graph Neural Networks by Modeling Noise Dependenciescited by 0
  7. 2025Robust Anomaly Detection with Graph Neural Networks using Controllabilitycited by 0