一种用于中介的分层潜在空间网络模型

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY

Network Science Pub Date : 2022-05-30 DOI:10.1017/nws.2022.12

T. Sweet, S. Adhikari

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引用次数: 2

摘要

摘要对于影响个人互动方式的干预措施，社交网络数据可能有助于理解干预措施有效的机制。社交网络甚至可能是研究结束前观察到的中间结果。事实上，社交网络也可能介导干预对兴趣结果的影响，Sweet（2019）引入了一个统计模型，将社交网络作为网络层面干预的中介。我们在他们的方法的基础上，引入了一个新的模型，其中网络是使用潜在空间方法的中介。我们通过模拟研究和对教师咨询网络的真实世界分析来研究我们的模型。

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A hierarchical latent space network model for mediation

Abstract For interventions that affect how individuals interact, social network data may aid in understanding the mechanisms through which an intervention is effective. Social networks may even be an intermediate outcome observed prior to end of the study. In fact, social networks may also mediate the effects of the intervention on the outcome of interest, and Sweet (2019) introduced a statistical model for social networks as mediators in network-level interventions. We build on their approach and introduce a new model in which the network is a mediator using a latent space approach. We investigate our model through a simulation study and a real-world analysis of teacher advice-seeking networks.

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来源期刊

Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

3.50

自引率

5.90%

发文量

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.