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Inferential Approaches for Network Analysis: AMEN for Latent Factor Models

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

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.

Suggested Citation

  • Minhas, Shahryar & Hoff, Peter D. & Ward, Michael D., 2019. "Inferential Approaches for Network Analysis: AMEN for Latent Factor Models," Political Analysis, Cambridge University Press, vol. 27(2), pages 208-222, April.
  • Handle: RePEc:cup:polals:v:27:y:2019:i:02:p:208-222_00
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    Cited by:

    1. Emily Kalah Gade & Michael Gabbay & Mohammed M. Hafez & Zane Kelly, 2019. "Networks of Cooperation: Rebel Alliances in Fragmented Civil Wars," Journal of Conflict Resolution, Peace Science Society (International), vol. 63(9), pages 2071-2097, October.
    2. Robert A. Blair & Nicholas Sambanis, 2020. "Forecasting Civil Wars: Theory and Structure in an Age of “Big Data†and Machine Learning," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(10), pages 1885-1915, November.
    3. James Adams & Simon Weschle & Christopher Wlezien, 2021. "Elite Interactions and Voters’ Perceptions of Parties’ Policy Positions," American Journal of Political Science, John Wiley & Sons, vol. 65(1), pages 101-114, January.
    4. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    5. Dass, Mayukh & Reshadi, Mehrnoosh & Li, Yuewu, 2023. "An exploration of ripple effects of advertising among major suppliers in a supply chain network," Journal of Business Research, Elsevier, vol. 169(C).
    6. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    7. Westra, Daan & Makai, Peter & Kemp, Ron, 2024. "Return to sender: Unraveling the role of structural and social network ties in patient sharing networks," Social Science & Medicine, Elsevier, vol. 340(C).
    8. Boucher, Vincent, 2020. "Equilibrium homophily in networks," European Economic Review, Elsevier, vol. 123(C).

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