Model transferability estimation is a task-adaptive pre-trained model selection problem, aiming to determine the optimal model for target dataset from a model hub pre-trained on source dataset without fine-tuning. Although existing model transferability evaluation methods have made some progress, they mainly focus on image or text data in CV and NLP. In contrast, the graph structural data with GNNs models is still underexplored, due to the complexity of the graph structure and the limitations of the generalization ability of GNN models under distribution shift. To fill this blank, we first propose a Graph Neural Network Model Transferability Estimation method via decomposition-augmented discriminant analysis, named GNNMTE, to evaluate the transferability of GNN models on target graph dataset without fine-tuning. It only calculates the GNNMTE score to determine whether it can be effectively transferred to target graph dataset and better select the optimal model for the target graph dataset. Specifically, our proposed \method contains three core components: (1) Dual-block SVD fusion for obtaining the corresponding principal component information; (2) Adaptive weighting by singular value ratio for guiding the extraction of important principal component information on graph data; (3) Graph discriminant analysis for finding the optimal projection direction that separates the classes of graph data. Extensive experimental results on cross-domain graph datasets achieve excellent results, demonstrating powerful superiority.