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Inferential Approaches for Network Analysis: AMEN for Latent Factor Models

Published online by Cambridge University Press:  20 November 2018

Shahryar Minhas ,
Peter D. Hoff  and
Michael D. Ward
Shahryar Minhas*
Affiliation:
Department of Political Science, Michigan State University, East Lansing, MI 48824, USA. Email: minhassh@msu.edu
Peter D. Hoff
Affiliation:
Department of Statistical Science, Duke University, Durham, NC 27701, USA. Email: peter.hoff@duke.edu
Michael D. Ward
Affiliation:
Department of Political Science, Duke University, Durham, NC 27701, USA. Email: michael.d.ward@duke.edu
*
*Email: minhassh@msu.edu
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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 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.

Keywords

latent variablesnetworksBayesian estimation

Information

Type
Articles
Information
Political Analysis , Volume 27 , Issue 2 , April 2019 , pp. 208 - 222
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

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 https://github.com/s7minhas/netmodels and on the Dataverse associated with this paper (Minhas, Hoff, and Ward 2018).

Contributing Editor: Jeff Gill

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