Skip to main content Accessibility help

Inferential Approaches for Network Analysis: AMEN for Latent Factor Models

  • Shahryar Minhas (a1), Peter D. Hoff (a2) and Michael D. Ward (a3)


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.


Corresponding author


Hide All

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 and on the Dataverse associated with this paper (Minhas, Hoff, and Ward 2018).

Contributing Editor: Jeff Gill



Hide All
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):634647.
Carnegie, Allison. 2014. States held hostage: Political hold-up problems and the effects of international institutions. American Political Science Review 108(01):5470.
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):237251.
Dafoe, Allan. 2011. Statistical critiques of the democratic peace: Caveat Emptor. American Journal of Political Science 55(2):247262.
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. 233240.
Diehl, Paul F., and Wright, Thorin M.. 2016. A conditional defense of the dyadic approach. International Studies Quarterly 60(2):363368.
Dixon, William. 1983. Measuring interstate affect. American Journal of Political Science 27(4):828851.
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):15161530.
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):919935.
Gollini, Isabella, and Murphy, Thomas B.. 2016. Joint modeling of multiple network views. Journal of Computational and Graphical Statistics 25(1):246265.
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.
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. 229252.
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.
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):129.
Ingold, Karin. 2008. Les mécanismes de décision: Le cas de la politique climatique Suisse. Politikanalysen . Zürich: Rüegger Verlag.
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 24:8898.
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.
Kinne, Brandon J. 2013. Network dynamics and the evolution of international cooperation. American Political Science Review 107(04):766785.
Krivitsky, Pavel N., and Handcock, Mark S.. 2015. latentnet: Latent position and cluster models for statistical networks. The Statnet Project ( R package version 2.7.1.
Lemke, Douglas, and Reed, William. 2001. War and rivalry among great powers. American Journal of Political Science 45(2):457469.
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):519535.
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):852877.
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):305321.
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):664689.
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):491505.
Minhas, Shahryar, Hoff, Peter D., and Ward, Michael D.. 2018 Replication data for: Inferential approaches for network analysis. AMEN for Latent Factor Models., Harvard Dataverse, V1.
Mitchell, Sara McLaughlin. 2002. A Kantian system? Democracy and third-party conflict resolution. American Journal of Political Science 46(4):749759.
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):15547660.
Nowicki, Krzysztof, and Snijders, Tom A. B.. 2001. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96(455):10771987.
Schweinberger, Michael. 2011. Instability, sensitivity, and degeneracy of discrete exponential families. Journal of the American Statistical Association 106(496):13611370.
Sewell, Daniel K., and Chen, Yuguo. 2015. Latent space models for dynamic networks. Journal of the American Statistical Association 110(512):16461657.
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):99153.
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 37:17421757.
Wasserman, Stanley, and Faust, Katherine. 1994. Social Network Analysis: Methods and Applications . Cambridge: Cambridge University Press.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *


Type Description Title
Supplementary materials

Minhas et al. supplementary material
Minhas et al. supplementary material 1

 Unknown (1.7 MB)
1.7 MB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed