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Graphon, short for graph function, provides a generative model for a network. In recent decades, various methods for graphon estimation have been proposed. The success of most graphon estimation methods depends on a proper specification of hyperparameters. Some network cross-validation methods have been proposed, but they suffer from restrictive model assumptions, expensive computational costs, and a lack of theoretical guarantees. To address these issues, we propose a graphon cross-validation (GraphonCV) method. Asymptotic properties of the GraphonCV are established. The effectiveness of the proposed method in terms of both computation and accuracy is demonstrated by extensive simulation studies and real drug repurposing examples