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

作     者：Hu, Xinxin Chen, Haotian Zhang, Junjie Chen, Hongchang Liu, Shuxin Li, Xing Wang, Yahui Xue, Xiangyang 

作者机构：National Digital Switching System Engineering and Technological Research Center Zhengzhou450002 China Department of Electrical & Computer Engineering University of Toronto TorontoM5S 3G4 Canada Institute of Big Data Fudan University Shanghai China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Embeddings 

摘      要：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://***/xxhu94/GAT-COBO. Copyright © 2023, The Authors. All rights reserved.