Traffic Analysis of Partially Observable Bearer Network under RouteNet based GNN Model

The expansion of the bearer network and the massive growth of network traffic data make it difficult to evaluate network performance. RouteNet proposes a graph neural network (GNN) model to solve such problems and achieves good results. Due to the black-box nature of GNNs, the relationship between topology and routing is not clear. This paper proposes a linear fitting model based on Graph Representation to represent the relationship between origin-destination (OD) traffic and link traffic. Based on this model, the paper proposes a traffic analysis scheme architecture for partially observable bearer networks. The architecture can analyze critical links and important OD traffic in the network. Based on this architecture, network node aggregation can also be performed, the network scale can be reduced, and the large-scale network performance evaluation problem can be solved. Numerical tests verify the accuracy and effectiveness of the proposed method in the traffic analysis problem of the bearer network.