title: Predicting co-author relationship in medical co-authorship networks.
abstract: Research collaborations are encouraged because a synergistic effect yielding good results often appears. However, creating and organizing a strong research group is a difficult task. One of the greatest concerns of an individual researcher is locating potential collaborators whose expertise complement his best. In this paper, we propose a method that makes link predictions in co-authorship networks, where topological features between authors such as Adamic/Adar, Common Neighbors, Jaccard's Coefficient, Preferential Attachment, Katzβ, and PropFlow may be good indicators of their future collaborations. Firstly, these topological features were systematically extracted from the network. Then, supervised models were used to learn the best weights associated with different topological features in deciding co-author relationships. Finally, we tested our models on the co-authorship networks in the research field of Coronary Artery Disease and obtained encouraging accuracy (the precision, recall, F1 score and AUC were, respectively, 0.696, 0.677, 0.671 and 0.742 for Logistic Regression, and respectively, 0.697, 0.678, 0.671 and 0.743 for SVM). This suggests that our models could be used to build and manage strong research groups.
authors.affiliation: Department of Medical Information Management, Shanxi Medical University, Taiyuan, Shanxi, China; School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Yu
authors.affiliation: School of Medicine, Stanford University, Stanford, California, United States of America.
authors.forename: Chao
authors.initials: C
authors.lastname: Long
authors.affiliation: Department of Medical Information Management, Shanxi Medical University, Taiyuan, Shanxi, China.
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Lv
authors.affiliation: Department of Science and Technology, Shanxi Medical University, Taiyuan, Shanxi, China.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Shao
authors.affiliation: Department of Medical Information Management, Shanxi Medical University, Taiyuan, Shanxi, China.
authors.forename: Peifeng
authors.initials: P
authors.lastname: He
authors.affiliation: School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
authors.forename: Zhiguang
authors.initials: Z
authors.lastname: Duan
mesh_terms: D001319:Authorship; D015706:Bibliometrics; D003299:Cooperative Behavior; D006801:Humans; D008962:Models, Theoretical; D011643:Publishing