Issue |
EPJ Web Conf.
Volume 244, 2020
Complexity and Disorder Meetings 2018-2019-2020
|
|
---|---|---|
Article Number | 01004 | |
Number of page(s) | 17 | |
DOI | https://doi.org/10.1051/epjconf/202024401004 | |
Published online | 15 October 2020 |
https://doi.org/10.1051/epjconf/202024401004
Searching for intermediaries An iterative structural deconstruction of personal networks
Aix Marseille Univ, CNRS, LEST, Aix en Provence, France.
* (claire.bidart@univ-amu.fr)
† CNRS, Tours. (anicalain.degenne@numericable.fr)
+ CNRS, EHESS, Université de Toulouse UT2J, LISSTCers. (michel.grossetti@univ-tlse2.fr)
Published online: 15 October 2020
Sociologists attempt to compare personal networks in order to identify explanatory factors that mayindicatedifferences betweenthem, aboveand beyond individual life histories. However, the usual indicators distinguish a single structural characteristic. In this article we attempt to find a more satisfactory way of explaining the structure of personal networks. We propose an iterative deconstruction of personal networks focused on betweenness centrality which is recalculated by an algorithm at each stage of the process. When applied to a sample of 287 cases gathered by direct interviews, it allows us to establish the betweennesses that are otherwise concealed by higher values, to determine the intermediaries at each stage, to record their exit rank, to characterise all the alters by several criteria and, finally, to take into consideration the number of stages required to deconstruct the network, thereby creating a new indicator of network stratification. These results are coherent with what we know about the evolutions of personal network structures and social roles along the life cycle. In a way, they reflect the imprints that time and life events have left on Ego’s network.
Key words: personal networks / structural analysis / methodology / indicators / betweenness centrality
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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