Inferential Approaches for Network Analyses: AMEN for Latent Factor Models

Shahryar Minhas; Peter D. Hoff; Michael D. Ward

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arXiv · 1611.00460

Inferential Approaches for Network Analyses: AMEN for Latent Factor Models

Shahryar Minhas·Peter D. Hoff·Michael D. Ward

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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 1st, 2nd, and 3rd 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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Shahryar Minhas, Peter D. Hoff, Michael D. Ward. 2016-11-02. Inferential Approaches for Network Analyses: AMEN for Latent Factor Models. https://arxiv.org/abs/1611.00460

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