Social network mediation analysis: A latent space approach

Haiyan Liu, Ick Hoon Jin, Zhiyong Zhang, Ying Yuan

Research output: Contribution to journalArticlepeer-review


Social networks contain data on both actor attributes and social connections among them. Such connections reflect the dependence among social actors, which is important for individual's mental health and social development. To investigate the potential mediation role of a social network, we propose a mediation model with a social network as a mediator. In the model, dependence among actors is accounted by a few mutually orthogonal latent dimensions. The scores on these dimensions are directly involved in the intervention process between an independent variable and a dependent variable. Because all the latent dimensions are equivalent in terms of their relationship to social networks, it is hardly to name them. The intervening effect through an individual dimension is thus of little practical interest. Therefore, we would rather focus on the mediation effect of a network. Although the scores are not unique, we rigorously articulate that the proposed network mediation effect is still well-defined. To estimate the model, we adopt a Bayesian estimation method. This modeling framework and the Bayesian estimation method is evaluated through a simulation study under representative conditions. Its usefulness is demonstrated through an empirical application to a college friendship network.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2018 Oct 8

All Science Journal Classification (ASJC) codes

  • General

Fingerprint Dive into the research topics of 'Social network mediation analysis: A latent space approach'. Together they form a unique fingerprint.

Cite this