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Co-authorship Prediction in the Author-keyword Bipartite Networks
Received date: 2016-07-28
Revised date: 2016-10-09
Online published: 2016-11-05
[Purpose/significance] This paper aims to clarify the influence of the content relationships based on author's keywords for co-authorship prediction and form the specialized indicators and methods in an author-keyword bipartite network, to improve the accuracy and interpretability of the co-authorship prediction.[Method/process] Firstly, the relationships between authors via keywords were represented by paths. The authors formed the co-authorship predictors with measurements of relations. Then, the logistic regression method was applied to learn the contributions of different paths for co-authorship prediction and the paths combination predictor was formed in an author-paper bipartite network. Finally, the predictors were quantitatively evaluated by the link prediction.[Result/conclusion] In the field of library and information science, the result confirms that the paths combination predictor performs best with far higher accuracy than other single path predictors. It also shows that the paths contribute differently to the co-authorship prediction where AKA is much more important than AKAKA. Furthermore, the predicted co-authorships are more easily interpreted by common interests denoted by keywords. Other paths will be added in the co-authorship prediction model and the generality of methods needs to be validated in other areas in the next step.
Zhang Jinzhu , Han Tao , Wang Xiaomei . Co-authorship Prediction in the Author-keyword Bipartite Networks[J]. Library and Information Service, 2016 , 60(21) : 74 -80 . DOI: 10.13266/j.issn.0252-3116.2016.21.010
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