Detecting leaders and key members of scientific teams in co-authorship networks

Recently most of the scientific studies have involved in a collaboration, team-based, and co-authorship approaches, which lead to knowledge production and high impact research outcomes. Previous studies lack to identify their real influential and productive nodes. We argue that investigating the structure of scientific teams with their leaders is equally essential as of the community structure. We formally define a scientific team leader as the most central member of a team. The proposed algorithm CLeader starts by initializing candidate leaders of a given co-authorship network. Consequently, we design a mathematical model to identify active and productive authors as real leaders, considering the publication year of their articles in a given period. Then, we iteratively discover subnetworks by grouping authors to their closest leaders and identify key members using DHRank. The experimental results indicate that the proposed algorithms outperform existing algorithms, and they are applicable in large-scale networks. (c) 2020 Elsevier Ltd. All rights reserved.