Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T06:05:46.027Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
Peter D. Hoff
Affiliation:
Department of Statistical Science, Duke University, Durham, NC 27701, USA. Email: [email protected]
Michael D. Ward
Affiliation:
Department of Political Science, Duke University, Durham, NC 27701, USA. Email: [email protected]
*
*Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

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
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. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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

References

Beck, Nathaniel, and Katz, Jonathan N.. 1995. What to do (and not to do) with pooled time-series cross-section data. American Political Science Review 89(3):634–647.Google Scholar
Carnegie, Allison. 2014. States held hostage: Political hold-up problems and the effects of international institutions. American Political Science Review 108(01):54–70.Google Scholar
Cranmer, Skyler J., Leifeld, Philip, McClurg, Scott D., and Rolfe, Meredith. 2017. Navigating the range of statistical tools for inferential network analysis. American Journal of Political Science 61(1):237–251.Google Scholar
Dafoe, Allan. 2011. Statistical critiques of the democratic peace: Caveat Emptor. American Journal of Political Science 55(2):247–262.Google Scholar
Davis, Jesse, and Goadrich, Mark. 2006. The relationship between Precision-Recall and ROC curves. In  Proceedings of the 23rd International Conference on Machine Learning . New York: Association for Computing Machinery, pp. 233–240.Google Scholar
Diehl, Paul F., and Wright, Thorin M.. 2016. A conditional defense of the dyadic approach. International Studies Quarterly 60(2):363–368.Google Scholar
Dixon, William. 1983. Measuring interstate affect. American Journal of Political Science 27(4):828–851.Google Scholar
Durante, Daniele, Dunson, David B., and Vogelstein, Joshua T.. 2017. Nonparametric Bayes modeling of populations of networks. Journal of the American Statistical Association 112(520):1516–1530.Google Scholar
Fuhrmann, Matthew, and Sechser, Todd S.. 2014. Signaling alliance commitments: Hand-tying and sunk costs in extended nuclear deterrence. American Journal of Political Science 58(4):919–935.Google Scholar
Gollini, Isabella, and Murphy, Thomas B.. 2016. Joint modeling of multiple network views. Journal of Computational and Graphical Statistics 25(1):246–265.Google Scholar
Goodreau, Steven M., Handcock, Mark S., Hunter, Carter T., Butts, David R, and Morris, Martina. 2008. A statnet tutorial. Journal of Statistical Software 24(9):1.Google Scholar
Handcock, Mark S. 2003. Statistical models for social networks: Inference and degeneracy. In Dynamic Social Network Modeling and Analysis , ed. Ronald, Breiger, Kathlene, Carley, and Pip, Pattison. Committee on Human Factors, Board on Behavioral, Cognitive, and Sensory Sciences, vol. 126. Washington, DC: National Academy Press, pp. 229–252.Google Scholar
Handcock, Mark S., Hunter, David R., Butts, Carter T., Goodreau, Steven M., and Morris, Martina. 2008. statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software 24(1):1548.Google Scholar
Hunter, David, Handcock, Mark, Butts, Carter, Goodreau, Steven M., and Morris, Martina. 2008. ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software 24(3):1–29.Google Scholar
Ingold, Karin. 2008. Les mécanismes de décision: Le cas de la politique climatique Suisse. Politikanalysen . Zürich: Rüegger Verlag.Google Scholar
Ingold, Karin, and Fischer, Manuel. 2014. Drivers of collaboration to mitigate climate change: An illustration of Swiss climate policy over 15 years. Global Environmental Change 24:88–98.Google Scholar
Kao, E. K., Smith, S. T., and Airoldi, E. M.. 2018. Hybrid mixed-membership blockmodel for inference on realistic network interactions. IEEE Transactions on Network Science and Engineering , doi:10.1109/TNSE.2018.2823324.Google Scholar
Kinne, Brandon J. 2013. Network dynamics and the evolution of international cooperation. American Political Science Review 107(04):766–785.Google Scholar
Krivitsky, Pavel N., and Handcock, Mark S.. 2015. latentnet: Latent position and cluster models for statistical networks. The Statnet Project (http://www.statnet.org). R package version 2.7.1. http://CRAN.R-project.org/package=latentnet.Google Scholar
Lemke, Douglas, and Reed, William. 2001. War and rivalry among great powers. American Journal of Political Science 45(2):457–469.Google Scholar
Li, Heng, and Loken, Eric. 2002. A unified theory of statistical analysis and inference for variance component models for dyadic data. Statistica Sinica 12(2):519–535.Google Scholar
Manger, Mark S., Pickup, Mark A., and Snijders, Tom A.B.. 2012. A hierarchy of preferences: A longitudinal network analysis approach to PTA formation. Journal of Conflict Resolution 56(5):852–877.Google Scholar
Mansfield, Edward, Milner, Helen V., and Rosendorff, B. Peter. 2000. Free to trade? Democracies, autocracies, and international trade negotiations. American Political Science Review 94(2):305–321.Google Scholar
Maoz, Zeev, Kuperman, Ranan D., Terris, Lesley, and Talmud, Ilan. 2006. Structural equivalence and international conflict: A social networks analysis. Journal of Conflict Resolution 50(5):664–689.Google Scholar
Minhas, Shahryar, Hoff, Peter D., and Ward, Michael D.. 2016. A new approach to analyzing coevolving longitudinal networks in international relations. Journal of Peace Research 53(3):491–505.Google Scholar
Minhas, Shahryar, Hoff, Peter D., and Ward, Michael D.. 2018 Replication data for: Inferential approaches for network analysis. AMEN for Latent Factor Models. https://doi.org/10.7910/DVN/H31DZG, Harvard Dataverse, V1.Google Scholar
Mitchell, Sara McLaughlin. 2002. A Kantian system? Democracy and third-party conflict resolution. American Journal of Political Science 46(4):749–759.Google Scholar
Morris, Martina, Handcock, Mark S., and Hunter, David R.. 2008. Specification of exponential-family random graph models: Terms and computational aspects. Journal of Statistical Software 24(4):1554–7660.Google Scholar
Nowicki, Krzysztof, and Snijders, Tom A. B.. 2001. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96(455):1077–1987.Google Scholar
Schweinberger, Michael. 2011. Instability, sensitivity, and degeneracy of discrete exponential families. Journal of the American Statistical Association 106(496):1361–1370.Google Scholar
Sewell, Daniel K., and Chen, Yuguo. 2015. Latent space models for dynamic networks. Journal of the American Statistical Association 110(512):1646–1657.Google Scholar
Snijders, Tom A. B., Pattison, Philippa E., Robins, Garry L., and Handcock, Mark S.. 2006. New specifications for exponential random graph models. Sociological Methodology 36(1):99–153.Google Scholar
Warner, Rebecca, Kenny, David, and Stoto, Michael. 1979. A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology 37:1742–1757.Google Scholar
Wasserman, Stanley, and Faust, Katherine. 1994. Social Network Analysis: Methods and Applications . Cambridge: Cambridge University Press.Google Scholar
Supplementary material: File

Minhas et al. supplementary material

Minhas et al. supplementary material 1

Download Minhas et al. supplementary material(File)
File 1.7 MB