This document discusses using graph neural networks (GNNs) to evaluate the performance of large-scale networks with topological flexibility. It introduces several techniques for enabling topological flexibility, including OpenScale, Flat-tree, and LEONet. GNNs are proposed to model these networks by mapping the network topology and traffic patterns onto the initial node feature matrix and adjacency matrix of the GNN. The results showed the GNN approach outperformed traditional methods in predicting end-to-end latency and network throughput, while being significantly less time-consuming.