mlsanityData to 14 Oct 2026

GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection

arXiv 2303.17334 ↗First posted 29 Mar 2023cs.LG cs.AIcited by 5

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Abstract (arXiv)

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a drastically increase in telecom fraud, which significantly dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, caused by the Pareto principle, brings severe challenges to graph data mining. This is a new and challenging problem, but little previous work has been noticed. In this paper, we propose a Graph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph imbalance problem. First, we design a GAT-based base classifier to learn the embeddings of all nodes in the graph. Then, we feed the embeddings into a well-designed cost-sensitive learner for imbalanced learning. Next, we update the weights according to the misclassification cost to make the model focus more on the minority class. Finally, we sum the node embeddings obtained by multiple cost-sensitive learners to obtain a comprehensive node representation, which is used for the downstream anomaly detection task. Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is also helpful for solving the widespread over-smoothing problem in GNNs. The GAT-COBO code and datasets are available at https://github.com/xxhu94/GAT-COBO.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 23 of its 79 references that are in the map. First posted on arXiv 29 Mar 2023.

Builds on

23 of 79 references.

References · 23

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11 more
  1. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  2. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
  3. 2017Network Representation Learning: A Surveycited by 101
  4. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 96
  5. 2018Bayesian graph convolutional neural networks for semi-supervised classificationcited by 89
  6. 2020Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detectioncited by 82
  7. 2018Imbalanced Deep Learning by Minority Class Incremental Rectificationcited by 65
  8. 2017Deep Over-sampling Framework for Classifying Imbalanced Datacited by 59
  9. 2019Spam Review Detection with Graph Convolutional Networkscited by 57
  10. 2022TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detectioncited by 18
  11. 2023Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detectioncited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2026Authority-Bound Governance of Heterogeneous AI Security Decisions in Telecom and IoT Networkscited by 1
  2. 2024Differential Encoding for Improved Representation Learning over Graphscited by 0
  3. 2025Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twincited by 0
  4. 2025One Size Fits All? A Modular Adaptive Sanitization Kit (MASK) for Customizable Privacy-Preserving Phone Scam Detectioncited by 0
  5. 2026Before the Warning Comes Too Late: Incremental Phone-Scam Detection from Speechcited by 0