Abstract 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.

中文翻译：

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在合着网络中检测科学团队的领导者和关键成员

摘要 最近，大多数科学研究都涉及合作、基于团队和合着的方法，这导致了知识生产和高影响力的研究成果。以前的研究缺乏确定他们真正有影响力和生产力的节点。我们认为，调查科学团队及其领导者的结构与社区结构同样重要。我们正式将科学团队负责人定义为团队中最核心的成员。所提出的算法 CLeader 首先初始化给定合着网络的候选领导者。因此，我们设计了一个数学模型，将活跃和富有成效的作者确定为真正的领导者，考虑到他们在特定时期的文章发表年份。然后，我们通过将作者分组到他们最近的领导者来迭代发现子网络，并使用 DHRank 识别关键成员。实验结果表明，所提出的算法优于现有算法，适用于大规模网络。