Graph Embedding-Based Fraud Nest Detection Method

With the increasing severity of telecommunications network fraud, this paper proposes a graph embedding-based fraud nest detection method aimed at utilizing artificial intelligence and graph neural network technology to identify and prevent telecommunications network fraud. The article first analyzes the challenges in current telecommunications network fraud detection, pointing out the limitations of rule-based expert systems and machine learning-based models. Subsequently, it details a new algorithm design, including graph embedding modules, graph aggregation modules, and Graph Neural Network (GNN) modules, which work together to identify fraudulent activities. By constructing a graph composed of nodes (users or devices) and edges (user interactions), GNN can capture global structural information in the data, identify implicit connections between users and devices, track abnormal behaviors and similar user groups, thereby identifying potential fraud gangs. The experimental part was conducted in a specific telecommunications network call environment, where users were treated as nodes and call behaviors as edges to construct a graph data structure. The effectiveness of the proposed method was verified through data cleaning, feature extraction, standardization, and dataset partitioning. Compared with models such as GCN, GAT, and RGCN, the GNN designed in this paper demonstrated superiority in accuracy, AUC, and recall rate. Finally, the paper summarizes the research findings and prospects for future work, emphasizing the practical significance of this method in fraud prevention and intervention.