GEEK-Explainer: An Efficient Interpretation Method for Graph Neural Networks in SDN

Shiyuan Liu, Chuanhuang Li, Chao Chen, Bo Ma, Zebin Chen, Xiaoyang Wang, Ying Zhang

Abstract: Graph neural networks (GNNs) have been widely applied in software-defined network (SDN) to enhance network modeling and performance forecasting. However, the closed-box nature of deep learning makes GNNs difficult to interpret, hindering their broad use and the application of GNN-based SDN systems in engineering. In this paper, we propose a novel interpretation framework named GEEK-Explainer, designed to efficiently provide instance-level interpretation of GNNs in SDN. Specifically, we introduce a KernelSHAP-based scoring module to generate intuitive and human-friendly explanations for each performance prediction. To address conflicts in computation cost, we propose a soft discrete mask matrix that identifies a critical set of important nodes. Extensive experiments demonstrate that the RouteNet model can effectively learn the relationships among features, which can provide a better understanding of the prediction process with less computation cost. These findings improve the transparency and robustness of the model and promote the application of GNN-based SDN systems in engineering practice.

External IDs:dblp:journals/tccn/LiuLCMCWZ26