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Inferential Approaches for Network Analysis: AMEN for Latent Factor Models

  • Shahryar Minhas (a1), Peter D. Hoff (a2) and Michael D. Ward (a3)

We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the exponential random graph model (ERGM). Relative to these approaches, we show that the AME approach is (a) to be easy to implement; (b) interpretable in a general linear model framework; (c) computationally straightforward; (d) not prone to degeneracy; (e) captures first-, second-, and third-order network dependencies; and (f) notably outperforms ERGMs and LSMs on a variety of metrics and in an out-of-sample context. In summary, AME offers a straightforward way to undertake nuanced, principled inferential network analysis for a wide range of social science questions.

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Authors’ note: Shahryar Minhas and Michael D. Ward acknowledge support from National Science Foundation (NSF) Award 1259266 and Peter D. Hoff acknowledges support from NSF Award 1505136. Replication files for this project can be accessed at and on the Dataverse associated with this paper (Minhas, Hoff, and Ward 2018).

Contributing Editor: Jeff Gill

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Political Analysis
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