AbstractWith deep learning creeping up into the ranks of big data, new models based on deep learning and massive data have made great leaps forward rapidly in the field of drug repositioning. However, there is no relevant review to summarize the transformations and development process of models and their data in the field of drug repositioning. Among all the computational methods, network‐based methods play an extraordinary role. In view of these circumstances, understanding and comparing existing network‐based computational methods applied in drug repositioning will help us recognize the cutting‐edge technologies and offer valuable information for relevant researchers. Therefore, in this review, we present an interpretation of the series of important network‐based methods applied in drug repositioning, together with their comparisons and development process.