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Detecting Heterogeneity and Inferring Latent Roles in Longitudinal Networks

Published online by Cambridge University Press:  31 July 2018

Benjamin W. Campbell Open the ORCID record for Benjamin W. Campbell [Opens in a new window]
Benjamin W. Campbell*
Affiliation:
Doctoral Candidate, Department of Political Science, The Ohio State University, Columbus, OH, USA. Email: campbell.1721@osu.edu
*
*Email: campbell.1721@osu.edu
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Abstract

Network analysis has typically examined the formation of whole networks while neglecting variation within or across networks. These approaches neglect the particular roles actors may adopt within networks. While cross-sectional approaches for inferring latent roles exist, there is a paucity of approaches for considering roles in longitudinal networks. This paper explores the conceptual dynamics of temporally observed roles while deriving and introducing a novel statistical tool, the ego-TERGM, capable of uncovering these latent dynamics. Estimated through an Expectation–Maximization algorithm, the ego-TERGM is quick and accurate in classifying roles within a broader temporal network. An application to the Kapferer strike network illustrates the model’s utility.

Keywords

networksmixture distributionsinterdependencetime-series datalatent variables
Type
Articles
Information
Political Analysis , Volume 26 , Issue 3 , July 2018 , pp. 292 - 311
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

The author is grateful to two anonymous reviewers, Janet Box-Steffensmeier, Bear Braumoeller, Skyler Cranmer, Dino Christenson, Christopher Gelpi, Jonathan Katz, Daniel Kent, Brendan Murphy, Andrew Rosenberg, Michael Salter-Townshend, and members of the Network Interdependence in Social Science lab at The Ohio State University for their thoughtful comments. Replication materials and the Supplementary Information (SI) Appendix can be found on the Political Analysis Dataverse (Campbell 2018).

Contributing Editor: Jonathan N. Katz

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