Inferential Approaches for Network Analysis: AMEN for Latent Factor Models

Shahryar Minhas, Peter D. Hoff and Michael D. Ward

Political Analysis, 2019, vol. 27, issue 2, 208-222

Abstract: 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:27:y:2019:i:02:p:208-222_00

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