Logistic network regression for scalable analysis of networks with joint edge/ vertex dynamics

Zack W. Almquist, Carter T. Butts

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

Original languageEnglish (US)
Pages (from-to)273-321
Number of pages49
JournalSociological Methodology
Volume44
Issue number1
DOIs
StatePublished - Jan 1 2014

Bibliographical note

Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the Office of Naval Research award N00014-08-1-1015, National Science Foundation awards BCS-0827027 and SES-1260798, and National Institute of Health/National Institute of Child Health and Human Development award 1R01HD068395-01.

Keywords

  • Dynamic networks
  • Dynamic prediction
  • Exponential family random graph models
  • Goodness of fit
  • Logistic regression
  • Model assessment
  • Vertex dynamics

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