Predicting Co-authorship with Combination of Paths in Paper-author Bipartite Networks
Zhang Jinzhu1(),Wang Xiaomei2,Han Tao2
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China 2National Science Library, Chinese Academy of Sciences, Beijing 100190, China
[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.
张金柱,王小梅,韩涛. 文献-作者二分网络中基于路径组合的合著关系预测研究*[J]. 现代图书情报技术, 2016, 32(10): 42-49.
Zhang Jinzhu,Wang Xiaomei,Han Tao. Predicting Co-authorship with Combination of Paths in Paper-author Bipartite Networks. New Technology of Library and Information Service, 2016, 32(10): 42-49.
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