科学合作与影响的微观层面网络动力学:合作作者网络分析的关系超事件模型

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
J. Lerner, Marian-Gabriel Hâncean
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引用次数: 5

摘要

摘要本文讨论了最近提出的一组统计网络模型——关系超事件模型(rhem),用于分析科学合著者网络中的团队选择和团队绩效。在共同作者网络研究中使用RHEM的基本原理是,科学合作本质上是多元的,也就是说,它通常涉及任何规模的团队。因此,RHEM指定了与代表任何规模的科学家群体的超边缘相关的发表率。超越之前关于会议数据的RHEM的工作,我们将这个模型族调整为关系超事件具有专用结果的设置,例如具有可测量影响的科学论文(例如,收到的引用数)。一方面,关系结果可以用来指定RHEM中的其他解释变量,因为共同创作的概率可能受到影响,例如,受到科学家先前(共享)成功的影响。另一方面,在寻求解释科学团队绩效的模型中,关系结果也可以作为响应变量。为了解决后者,我们提出了与RHEM密切相关的关系超事件结果模型,以至于两个模型家族都可以分别指定科学合作的可能性和预期性能,使用相同的解释变量集允许评估,例如,导致合作增加的变量是否也倾向于增加科学影响。为了说明这一点,我们将RHEM应用于实证合著者网络,该网络由来自三个科学学科的科学家发表的35万多篇论文组成。我们的模型解释了科学合作和影响,其中包括个人活动(优先依恋)、共享活动(熟悉)、三合一封闭、先前的个人和共享成功,以及超边缘成员之间的先前成功差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks
Abstract We discuss a recently proposed family of statistical network models—relational hyperevent models (RHEMs)—for analyzing team selection and team performance in scientific coauthor networks. The underlying rationale for using RHEM in studies of coauthor networks is that scientific collaboration is intrinsically polyadic, that is, it typically involves teams of any size. Consequently, RHEM specify publication rates associated with hyperedges representing groups of scientists of any size. Going beyond previous work on RHEM for meeting data, we adapt this model family to settings in which relational hyperevents have a dedicated outcome, such as a scientific paper with a measurable impact (e.g., the received number of citations). Relational outcome can on the one hand be used to specify additional explanatory variables in RHEM since the probability of coauthoring may be influenced, for instance, by prior (shared) success of scientists. On the other hand, relational outcome can also serve as a response variable in models seeking to explain the performance of scientific teams. To tackle the latter, we propose relational hyperevent outcome models that are closely related with RHEM to the point that both model families can specify the likelihood of scientific collaboration—and the expected performance, respectively—with the same set of explanatory variables allowing to assess, for instance, whether variables leading to increased collaboration also tend to increase scientific impact. For illustration, we apply RHEM to empirical coauthor networks comprising more than 350,000 published papers by scientists working in three scientific disciplines. Our models explain scientific collaboration and impact by, among others, individual activity (preferential attachment), shared activity (familiarity), triadic closure, prior individual and shared success, and prior success disparity among the members of hyperedges.
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来源期刊
Network Science
Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.50
自引率
5.90%
发文量
24
期刊介绍: Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
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