mlsanityData to 14 Oct 2026

Deep Fraud Detection on Non-attributed Graph

arXiv 2110.01171 ↗First posted 4 Oct 2021cs.LG cs.SIcited by 3

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Abstract (arXiv)

Fraud detection problems are usually formulated as a machine learning problem on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance on fraud detection. The successes of most previous methods heavily rely on rich node features and high-fidelity labels. However, labeled data is scarce in large-scale industrial problems, especially for fraud detection where new patterns emerge from time to time. Meanwhile, node features are also limited due to privacy and other constraints. In this paper, two improvements are proposed: 1) We design a graph transformation method capturing the structural information to facilitate GNNs on non-attributed fraud graphs. 2) We propose a novel graph pre-training strategy to leverage more unlabeled data via contrastive learning. Experiments on a large-scale industrial dataset demonstrate the effectiveness of the proposed framework for fraud detection.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 4 Oct 2021.

Builds on

17 of 24 references.

References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 463
  9. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  10. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  11. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 202
  12. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 96
5 more
  1. 2019Spam Review Detection with Graph Convolutional Networkscited by 57
  2. 2020GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detectioncited by 54
  3. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  4. 2019On Node Features for Graph Neural Networkscited by 21
  5. 2021Unsupervised Outlier Detection using Memory and Contrastive Learningcited by 11

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.

  1. 2022Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlookcited by 24
  2. 2024Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating Mechanismcited by 1
  3. 2025A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platformscited by 0