Exploring the Limitations of Current Graph Neural Networks for Network Modeling

Graph neural networks (GNN) have recently been proposed as a technique for accurate and cost-efficient network modeling. As an example, the GNN-based model RouteNet has shown potential for network performance evaluation, being the first-of-its-kind Machine-Learning-based model with generalization capabilities to other networks and configurations unseen during training.In this paper we assess the generalization limits of RouteNet, by analyzing how different network parameters affect the accuracy of this model. To this end, we systematically evaluate the accuracy of RouteNet under modifications of properties of the network and the traffic, such as the topology size, link capacities, the packet size distribution, and the network congestion level. We determine that, while this GNN model is robust to changes in the structure of its input graph, the quality of the estimates degrades considerably, when the distributions of the predicted values of the evaluation data differ from the training (e.g., end-to-end delays). As a result, we argue that to achieve practical GNN-based solutions for network modeling, new methods are needed that can, for example, cope with traffic loads and network sizes that are significantly different than those seen during training.