  Identification of critical nodes is a prominent topic in the study of complex
networks. Numerous methods have been proposed, yet most exhibit inherent
limitations. Traditional approaches primarily analyze specific structural
features of the network; however, node influence is typically the result of a
combination of multiple factors. Machine learning-based methods struggle to
effectively represent the complex characteristics of network structures through
suitable embedding techniques and require substantial data for training,
rendering them prohibitively costly for large-scale networks. To address these
challenges, this paper presents an active learning model based on GraphSAGE and
Transformer, named GNNTAL. This model is initially pre-trained on random or
synthetic networks and subsequently fine-tuned on real-world networks by
selecting a few representative nodes using K-Means clustering and uncertainty
sampling. This approach offers two main advantages: (1) it significantly
reduces training costs; (2) it simultaneously incorporates both local and
global features. A series of comparative experiments conducted on twelve
real-world networks demonstrate that GNNTAL achieves superior performance.
Additionally, this paper proposes an influence maximization method based on the
predictions of the GNNTAL model, which achieves optimal performance without the
need for complex computations. Finally, the paper analyses certain limitations
of the GNNTAL model and suggests potential solutions.
