Mediation analysis is increasingly used in the social sciences. Extension to social network data, however, has proved difficult because statistical network models are formulated at a lower level of analysis (the dyad) than many outcomes of interest. This study introduces a general approach for micro-macro mediation analysis in social networks. The author defines the average mediated micro effect (AMME) as the indirect effect of a network selection process on an individual, group, or organizational outcome through its effect on an intervening network variable. The author shows that the AMME can be nonparametrically identified using a wide range of common statistical network and regression modeling strategies under the assumption of conditional independence among multiple mediators. Nonparametric and parametric algorithms are introduced to generically estimate the AMME in a multitude of research designs. The author illustrates the utility of the method with an applied example using cross-sectional National Longitudinal Study of Adolescent to Adult Health data to examine the friendship selection mechanisms that indirectly shape adolescent school performance through their effect on network structure.

中文翻译：

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社交网络中的微观-宏观中介分析

中介分析越来越多地应用于社会科学中。然而，事实证明，扩展到社交网络数据很困难，因为统计网络模型是在比许多感兴趣的结果更低的分析水平（二元组）上制定的。本研究介绍了社交网络中微观宏观中介分析的通用方法。作者将平均介导微观效应（AMME）定义为网络选择过程通过其对干预网络变量的影响而对个人、群体或组织结果产生的间接影响。作者表明，在多个中介变量之间条件独立的假设下，可以使用广泛的常见统计网络和回归建模策略来非参数地识别 AMME。引入非参数和参数算法来一般性地估计大量研究设计中的 AMME。作者通过一个应用示例说明了该方法的实用性，该示例使用横断面国家青少年到成人健康纵向研究数据来检查友谊选择机制，该机制通过对网络结构的影响间接影响青少年在学校的表现。