A network-based and multi-parameter model for finding influential authors

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 alpha provides useful information exogenous to the established network, and parameter beta 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 Erdos, a comprehensive application of this new model is also provided. (C) 2014 Elsevier Ltd. All rights reserved.