Co-evolution of a socio-cognitive scientific network: A case study of citation dynamics among astronomers

IF 2.9 2区 社会学 Q1 ANTHROPOLOGY Social Networks Pub Date : 2023-12-23 DOI:10.1016/j.socnet.2023.11.008
Alejandro Espinosa-Rada , Elisa Bellotti , Martin G. Everett , Christoph Stadtfeld
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Abstract

This paper aims to understand how a group of academics cite each others’ work through time, considering the simultaneous co-evolution of three networks representing their scientific collaboration, the journals in which they publish and institutional membership. It argues that both social and cognitive processes contribute to these dynamics. Two types of network mechanisms are considered specifically: closures by affiliation and closures by association. To assess whether these mechanisms generate the macro features of the network under study, we propose new features for three-mode multilevel networks such as the mixed geodesic distances, mixed degree distributions, and the mixed quadrilateral census. We investigate whether a micro-level model that considers the above-mentioned network mechanisms is able to correctly reproduce these features. We apply stochastic actor-oriented models (SAOMs) for one-mode and two-mode networks to link the micro-macro processes using a dataset of a scientific community of astronomers from 2013 to 2015. The results suggest that social relationships grounded on scientific collaboration and proximity based on institutional affiliation are more accurately suited to understanding the co-evolution of the network of citations than an alternative approach that merely considers cognitive-based networks measured as the similarity in publishing in the same journals.

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社会认知科学网络的共同进化:天文学家引用动态案例研究
本文旨在了解一群学者如何在一段时间内相互引用对方的研究成果,同时考虑到代表其科学合作、发表论文的期刊和机构成员资格的三个网络的共同演变。文章认为,社会和认知过程都有助于这些动态变化。本研究特别考虑了两类网络机制:隶属关系的封闭和关联关系的封闭。为了评估这些机制是否会产生所研究网络的宏观特征,我们提出了三模式多层次网络的新特征,如混合大地距离、混合度分布和混合四边形普查。我们研究了考虑上述网络机制的微观模型是否能够正确再现这些特征。我们使用 2013 年至 2015 年天文学家科学社区的数据集,应用单模和双模网络的随机行为者导向模型(SAOMs),将微观和宏观过程联系起来。结果表明,以科学合作为基础的社会关系和以机构隶属关系为基础的邻近性,比仅仅考虑以在相同期刊上发表论文的相似性为衡量标准的基于认知的网络的替代方法,更适合理解引文网络的共同演化。
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来源期刊
Social Networks
Social Networks Multiple-
CiteScore
5.90
自引率
12.90%
发文量
118
期刊介绍: Social Networks is an interdisciplinary and international quarterly. It provides a common forum for representatives of anthropology, sociology, history, social psychology, political science, human geography, biology, economics, communications science and other disciplines who share an interest in the study of the empirical structure of social relations and associations that may be expressed in network form. It publishes both theoretical and substantive papers. Critical reviews of major theoretical or methodological approaches using the notion of networks in the analysis of social behaviour are also included, as are reviews of recent books dealing with social networks and social structure.
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