Research on structural feature learning and fault prediction method of communication network based on graph neural network

Traditional fault prediction methods are difficult to meet the needs of modern communication networks because of the limitations of static model, dependence on a single data source and lack of real-time processing ability. Graph neural network (GNN) can effectively capture the complex relationship between network devices and improve the accuracy and efficiency of fault prediction by virtue of its topological awareness, dynamic modeling potential and multi-modal integration advantages. In this paper, a cross-modal fusion method based on multi-scale dynamic GNN (MSGNN-CM) is proposed. The communication network is modeled as a dynamic graph, and the local and global topological features are extracted through the convolution of multi-scale graphs. The time-series evolution of network state is captured by Gated Recurrent Unit (GRU), and the topology, time-series and alarm information are fused through attention mechanism, so as to realize node-level fault probability prediction and critical link risk identification. This method has the advantages of multi-scale perception, dynamic adaptability and multi-source collaborative integration, and can effectively cope with the dynamic changes and complexity of the network. The experiment uses six months' operation and maintenance data of three city base stations, involving 500 nodes and 2,100 edges. The characteristics of nodes include traffic, delay and CPU load, while the characteristics of edges include bandwidth utilization and packet loss rate. The results show that MSGNN-CM performs best in node-level fault prediction tasks, with F1-Score and AUC-ROC reaching 88.6% and 92.3% respectively, which are significantly better than traditional methods and existing GNN models. In addition, this method also performs well in link-level risk detection, with Precision@10 and Precision@20 reaching 53.8% and 45.2% respectively. The analysis of dynamic adaptability shows that choosing a suitable time window is very important to the performance of the model, and GRU mechanism has the ability to respond quickly to sudden events. Explanatory verification shows that the cross-modal attention mechanism can effectively identify key failure indicators and improve the transparency and practicability of the model.