[Objective] This paper aims to predict co-authorship more effectively and reduce the information loss. [Methods] First, we constructed a paper-author bipartite network and its co-authorship counterpart in the field of library and information science. Second, we described the relationships among authors with the path-length of two and three from the bipartite network. Third, we used the logistic regression method to learn the influence of different factors. Finally, we predicted co-authorship in the paper-author bipartite network with various indictors. [Results] We found significant information loss in the change from the paper-author bipartite network to the co-authorship network. The logistic regression method was an appropriate way to learn the contributions of paths. The new indicators were more accurate and the predicted co-authorships could be interpreted more easily. [Limitations] We did not include the multiple paths methods to the present study and more research is needed to examine the proposed method in other areas. [Conclusions] Co-authorship prediction should be conducted in the paper-author bipartite network to reduce the information loss. The paths combination indicator in the paper-author bipartite network might be the most effective method to predict co-authorship, which could be applied to the patent-inventor bipartite network.