mlsanityData to 7 Oct 2026

Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View

arXiv 2212.00535 ↗First posted 1 Dec 2022cs.LGcited by 54

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Abstract (arXiv)

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. To be specific, we regard the original input graph as the first view and generate the second view by graph augmentation with edge modifications. With the guidance of maximizing the similarity of the subgraph pairs, the proposed subgraph-subgraph contrast contributes to more robust subgraph embeddings despite of the structure variation. Moreover, the introduced subgraph-subgraph contrast cooperates well with the widely-adopted node-subgraph and node-node contrastive counterparts for mutual GAD performance promotions. Besides, we also conduct sufficient experiments to investigate the impact of different graph augmentation approaches on detection performance. The comprehensive experimental results well demonstrate the superiority of our method compared with the state-of-the-art approaches and the effectiveness of the multi-view subgraph pair contrastive strategy for the GAD task.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Builds on 32 of its 59 references that are in the map. First posted on arXiv 1 Dec 2022.

Builds on

32 of 59 references.

References · 32

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  5. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  6. 2018Deep Graph Infomaxcited by 1,101
  7. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  8. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 461
  9. 2019Diffusion Improves Graph Learningcited by 414
  10. 2021Graph Self-Supervised Learning: A Surveycited by 289
  11. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  12. 2020Graph Random Neural Network for Semi-Supervised Learning on Graphscited by 213
20 more
  1. 2020Data Augmentation for Graph Neural Networkscited by 200
  2. 2022Rethinking Graph Neural Networks for Anomaly Detectioncited by 185
  3. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  4. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  5. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  6. 2020Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learningcited by 90
  7. 2020Deep Fusion Clustering Networkcited by 71
  8. 2021Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learningcited by 70
  9. 2021Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detectioncited by 67
  10. 2022Graph Data Augmentation for Graph Machine Learning: A Surveycited by 61
  11. 2022Simple Contrastive Graph Clusteringcited by 56
  12. 2021Rethinking Graph Auto-Encoder Models for Attributed Graph Clusteringcited by 37
  13. 2016Scalable Anomaly Ranking of Attributed Neighborhoodscited by 33
  14. 2021Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networkscited by 31
  15. 2022Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networkscited by 28
  16. 2022Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learningcited by 23
  17. 2022Mixed Graph Contrastive Network for Semi-Supervised Node Classificationcited by 21
  18. 2022Improved Dual Correlation Reduction Networkcited by 15
  19. 2022RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learningcited by 12
  20. 2022Generative Subgraph Contrast for Self-Supervised Graph Representation Learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 19; 2025: 14; 2026: 9.

54 papers.

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  1. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  2. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  3. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  4. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  5. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  6. 2023PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detectioncited by 25
  7. 2025A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detectioncited by 24
  8. 2023ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detectioncited by 23
  9. 2023Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classificationcited by 19
  10. 2023Reinforcement Graph Clustering with Unknown Cluster Numbercited by 16
  11. 2024UniGAD: Unifying Multi-level Graph Anomaly Detectioncited by 13
  12. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
38 more
  1. 2024SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detectioncited by 11
  2. 2024Cluster Aware Graph Anomaly Detectioncited by 7
  3. 2025Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspectivecited by 7
  4. 2023Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learningcited by 6
  5. 2023Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detectioncited by 6
  6. 2024Towards Cross-domain Few-shot Graph Anomaly Detectioncited by 5
  7. 2023Revisiting Graph Contrastive Learning for Anomaly Detectioncited by 4
  8. 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learningcited by 4
  9. 2025Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learningcited by 4
  10. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  11. 2024Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detectioncited by 3
  12. 2025Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detectioncited by 3
  13. 2026GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detectioncited by 3
  14. 2024UMGAD: Unsupervised Multiplex Graph Anomaly Detectioncited by 2
  15. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  16. 2026Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detectioncited by 2
  17. 2023Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graphcited by 1
  18. 2023MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detectioncited by 1
  19. 2023Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,cited by 1
  20. 2024SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networkscited by 1
  21. 2025ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networkscited by 1
  22. 2026OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detectioncited by 1
  23. 2026AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detectioncited by 1
  24. 2026RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detectioncited by 1
  25. 2023A Graph Encoder-Decoder Network for Unsupervised Anomaly Detectioncited by 0
  26. 2024Detecting Anomalies in Dynamic Graphs via Memory enhanced Normalitycited by 0
  27. 2024GLADformer: A Mixed Perspective for Graph-level Anomaly Detectioncited by 0
  28. 2024Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labelscited by 0
  29. 2024LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Spacecited by 0
  30. 2024Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detectioncited by 0
  31. 2025Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detectioncited by 0
  32. 2025Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learningcited by 0
  33. 2025Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspectivecited by 0
  34. 2025Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detectioncited by 0
  35. 2025GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluationcited by 0
  36. 2025Normality Calibration in Semi-supervised Graph Anomaly Detectioncited by 0
  37. 2025Combining Euclidean and Hyperbolic Representations for Node-level Anomaly Detectioncited by 0
  38. 2025Explainable Graph Representation Learning via Graph Pattern Analysiscited by 0

50 most cited of 54 All 54 (JSON)