A network-based and multi-parameter model for finding influential authors |
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| Institution: | 1. School of Management, Harbin Institute of Technology, Harbin 150001, PR China;2. Dipartimento Di Economia Politica E Statistica, Università Di Siena, Siena 53100, Italy;3. School of Software, Harbin Institute of Technology, Harbin 150001, PR China;1. CNRS (LAMSADE, UMR 7243) & Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, F-75775 Paris Cedex 16, France;2. Ghent University, Department of Data Analysis, H. Dunantlaan, 1, B-9000 Gent, Belgium;1. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong;2. Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd., Singapore |
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| Abstract: | This study proposes a network-based model with two parameters to find influential authors based on the idea that the prestige of a whole network changes when a node is removed. We apply the Katz–Bonacich centrality to define network prestige, which agrees with the idea behind the PageRank algorithm. We further deduce a concise mathematical formula to calculate each author's influence score to find the influential ones. Furthermore, the functions of two parameters are revealed by the analysis of simulation and the test on the real-world data. Parameter α provides useful information exogenous to the established network, and parameter β measures the robustness of the result for cases in which the incompleteness of the network is considered. On the basis of the coauthor network of Paul Erdös, a comprehensive application of this new model is also provided. |
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| Keywords: | Author's influence Network analysis Coauthor network Multi-parameter model Simulation Evaluation techniques for scientific output |
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