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Structure in personal networks: Constructing and comparing typologies

Published online by Cambridge University Press:  04 November 2019

Raffaele Vacca Open the ORCID record for Raffaele Vacca [Opens in a new window]
Raffaele Vacca*
Affiliation:
Department of Sociology and Criminology & Law, University of Florida, Email: r.vacca@ufl.edu
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Abstract

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A recurrent finding in personal network research is that individual and social outcomes are influenced not just by the kind of people one knows, but also by how those people are connected to each other. Personal network structure – the way in which one’s personal contacts know and interact with each other – reflects broader trends in social organization and personal communities, and shapes patterns of social capital, support, and isolation. This article proposes a method to identify typologies of structure in large collections of personal networks. The method is applied to six datasets collected in widely different circumstances and using various survey instruments. It is then compared with another recently introduced method to extract typologies of egocentric network structure. Findings show that personal network structure can be effectively summarized using just three measures of cohesive subgroup characteristics. Structural typologies can then be identified by applying standard cluster analysis techniques to the three variables. Both methods considered in the article capture significant variation in network structures, but they also show substantial levels of disagreement and cross-classification. I discuss similarities and differences between the methods, and potential applications of the proposed typologies to substantive research on personal communities, social support, and social capital.

Keywords

Brea L. PerryBernice A. PescosolidoMario L. SmallAnn McCranieego-networkssocial capitalsocial supportsocial isolationpersonal communitiescommunity detectionmodularitycore/peripherycluster analysisk-medoid

Information

Type
Research Article
Information
Network Science , Volume 8 , Issue 2: Special Issue Ego Networks , June 2020 , pp. 142 - 167
Copyright
© Cambridge University Press 2019

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