Shapley Explainer - An Interpretation Method for GNNs Used in SDN

Abstract

Graph neural networks (GNNs) have been widely applied in software-defined network (SDN) for better network modeling and performance prediction. However, the black-box characteristic of deep learning makes the GNNs hard to interpret, such interpretability issue hinders the wide use of GNNs. In this paper, we propose Shapley Explainer, that provides fair importance scores to the input nodes of a GNN within an appropriate computation cost, thereby providing a valid and reasonable interpretation of graph neural network on software defined network. The proposed method derives the importance ranking of topological nodes by combining shapley values with a soft discrete mask matrix. We apply Shapley Explainer to RouteNet model, a GNN model that provides intelligent predictions of SDN network performance metrics. The experimental results show that Shapley Explainer can provide effective interpretations for RouteNet. It also verifies that the RouteNet model can correctly learn the relationship between features, which can provide a better understanding of the prediction process of RouteNet, promoting the application of GNN-based SDN systems in engineering practice.