TY - JOUR
T1 - Eigenvector centralization as a measure of structural bias in information aggregation
AU - Bienenstock, Elisa Jayne
AU - Bonacich, Phillip
N1 - Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - The principal eigenvector of the adjacency matrix is widely used to complement degree, betweenness and closeness measures of network centrality. Employing eigenvector centrality as an individual level metric underutilizes this measure. Here we demonstrate how eigenvector centralization, used as a network-level metric, models the potential, or limitation, for the diffusion of novel information within a network. We relate eigenvector centralization to assortativity and core–periphery and use simple simulations to demonstrate how eigenvector centralization is ideal for revealing the conditions under which network structure produces suboptimal utilization of available information. Our findings provide a structural explanation for the persistence of “out of touch” business and political leadership even when organizations implement protocols and interventions to improve leadership accessibility.
AB - The principal eigenvector of the adjacency matrix is widely used to complement degree, betweenness and closeness measures of network centrality. Employing eigenvector centrality as an individual level metric underutilizes this measure. Here we demonstrate how eigenvector centralization, used as a network-level metric, models the potential, or limitation, for the diffusion of novel information within a network. We relate eigenvector centralization to assortativity and core–periphery and use simple simulations to demonstrate how eigenvector centralization is ideal for revealing the conditions under which network structure produces suboptimal utilization of available information. Our findings provide a structural explanation for the persistence of “out of touch” business and political leadership even when organizations implement protocols and interventions to improve leadership accessibility.
KW - Assortativity
KW - Centralization
KW - Eigenvector Centrality
KW - Social Networks
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U2 - 10.1080/0022250X.2021.1878357
DO - 10.1080/0022250X.2021.1878357
M3 - Article
AN - SCOPUS:85101650086
SN - 0022-250X
VL - 46
SP - 227
EP - 245
JO - Journal of Mathematical Sociology
JF - Journal of Mathematical Sociology
IS - 3
ER -