2021
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van Kuppevelt, Dafne E; Bakhshi, Rena; Heemskerk, Eelke M.; Takes, Frank W. Community membership consistency applied to corporate board interlock networks Journal Article In: Journal of Computational Social Science, 2021. @article{vanKuppevelt2021,
title = {Community membership consistency applied to corporate board interlock networks},
author = {Dafne E van Kuppevelt and Rena Bakhshi and Eelke M. Heemskerk and Frank W. Takes},
url = {DOI: 10.1007/s42001-021-00145-5
https://link.springer.com/article/10.1007/s42001-021-00145-5#article-info},
doi = {10.1007/s42001-021-00145-5},
year = {2021},
date = {2021-11-19},
journal = {Journal of Computational Social Science},
abstract = {Community detection is a well-established method for studying the meso-scale
structure of social networks. Applying a community detection algorithm results in
a division of a network into communities that is often used to inspect and reason
about community membership of specifc nodes. This micro-level interpretation step
of community structure is a crucial step in typical social science research. However,
the methodological caveat in this step is that virtually all modern community detection methods are non-deterministic and based on randomization and approximated
results. This needs to be explicitly taken into consideration when reasoning about
community membership of individual nodes. To do so, we propose a metric of community membership consistency, that provides node-level insights in how reliable
the placement of that node into a community really is. In addition, it enables us to
distinguish the community core members of a community. The usefulness of the
proposed metrics is demonstrated on corporate board interlock networks, in which
weighted links represent shared senior level directors between frms. Results suggest
that the community structure of global business groups is centered around persistent
communities consisting of core countries tied by geographical and cultural proximity. In addition, we identify fringe countries that appear to associate with a number
of diferent global business communities.},
keywords = {board interlocks, community detection, interlocking directorates, modularity, network analysis},
pubstate = {published},
tppubtype = {article}
}
Community detection is a well-established method for studying the meso-scale
structure of social networks. Applying a community detection algorithm results in
a division of a network into communities that is often used to inspect and reason
about community membership of specifc nodes. This micro-level interpretation step
of community structure is a crucial step in typical social science research. However,
the methodological caveat in this step is that virtually all modern community detection methods are non-deterministic and based on randomization and approximated
results. This needs to be explicitly taken into consideration when reasoning about
community membership of individual nodes. To do so, we propose a metric of community membership consistency, that provides node-level insights in how reliable
the placement of that node into a community really is. In addition, it enables us to
distinguish the community core members of a community. The usefulness of the
proposed metrics is demonstrated on corporate board interlock networks, in which
weighted links represent shared senior level directors between frms. Results suggest
that the community structure of global business groups is centered around persistent
communities consisting of core countries tied by geographical and cultural proximity. In addition, we identify fringe countries that appear to associate with a number
of diferent global business communities. |