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Clustering ensembles of social networks

Published online by Cambridge University Press:  15 April 2019

Tracy M. Sweet Open the ORCID record for Tracy M. Sweet [Opens in a new window] ,
Abby Flynt  and
David Choi
Tracy M. Sweet*
Affiliation:
Department of Human Development and Quantitative MethodologyUniversity of Maryland College Park, MD, USA
Abby Flynt
Affiliation:
Department of MathematicsBucknell University Lewisburg, PA, USA
David Choi
Affiliation:
Heinz College Carnegie Mellon University Pittsburgh, PA, USA
*
*Corresponding author. Email: [email protected]
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Abstract

Recently there has been significant work in the social sciences involving ensembles of social networks, that is, multiple, independent, social networks such as students within schools or employees within organizations. There remains, however, very little methodological work on exploring these types of data structures. We present methods for clustering social networks with observed nodal class labels, based on statistics of walk counts between the nodal classes. We extend this method to consider only non-backtracking walks, and introduce a method for normalizing the counts of long walk sequences using those of shorter ones. We then present a method for clustering networks based on these statistics to explore similarities among networks. We demonstrate the utility of this method on simulated network data, as well as on advice-seeking networks in education.

Keywords

clusteringgraph kernelrandom walksadvice networkseducation
Type
Original Article
Information
Network Science , Volume 7 , Issue 2 , June 2019 , pp. 141 - 159
Copyright
© Cambridge University Press 2019 

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