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Inferential Approaches for Network Analysis: AMEN for Latent Factor Models.
In: Political Analysis, Jg. 27 (2019-04-01), Heft 8, S. 208-222
Online
academicJournal
Zugriff:
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. [ABSTRACT FROM AUTHOR]
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Inferential Approaches for Network Analysis: AMEN for Latent Factor Models.
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Autor/in / Beteiligte Person: | Minhas, Shahryar ; Hoff, Peter D. ; Ward, Michael D. |
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Zeitschrift: | Political Analysis, Jg. 27 (2019-04-01), Heft 8, S. 208-222 |
Veröffentlichung: | 2019 |
Medientyp: | academicJournal |
ISSN: | 1047-1987 (print) |
DOI: | 10.1017/pan.2018.50 |
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