Influence Maximization (IM) is a crucial problem in social network analysis and has been extensively studied. Traditional approaches rely on designing approximation algorithms using network sampling; however, these methods lack generalizability and depend on an explicit definition of the influence diffusion model as input. Recently, researchers have turned to deep learning methods to address the shortcomings of traditional IM algorithms, but current learning-based IM algorithms still suffer from severe deficiencies in scalability and generalizability. In this paper, we propose IMGNN, a simple, efficient, effective, and generalizable algorithm powered by graph neural networks. IMGNN is a learning-based IM algorithm with strong generalization capability that reduces the overhead of model retraining while also providing fast seed set inference speed. As a result, IMGNN achieves better performance in terms of both efficiency and effectiveness compared to existing IM algorithms. Its exceptional generalization capability also enables it to be trained on small-scale graphs and directly infer the seed node set for large-scale graphs. IMGNN achieves these advantages by adopting a novel design for feature construction and model training, utilizing features constructed from influence propagations over graphs with randomly skipped nodes. This approach enables IMGNN to avoid overfitting to specific network structures while employing a unique technique to improve time efficiency by training on smaller networks. We have conducted extensive experiments using real-world social networks with up to 40 million nodes, and the results strongly demonstrate the superiority of IMGNN in terms of influence spread, seed node set inference speed, and generalizability.