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Eigenvector centralization as a measure of structural bias in information aggregation

Bibliographic Data

ID7518965
AuthorsE J Bienenstock (0000-0001-8747-8872, Arizona State University, corresponding author), Phillip Bonacich (Department of Sociology, University of California, Los Angeles, California, USA)
Year2021
Volume46
Issue3
Pages227-245
Publication date2021-02-25
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Mathematical Sociology (JOURNAL)
Journal identifiersISSN: 0022-250X • E-ISSN: 1545-5874
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/0022250x.2021.1878357
OpenAlexW3135369915
LanguageEN
Citations received10
References cited26

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

Adjacency matrix · Assortativity · Betweenness centrality · Centrality · Closeness · Combinatorics · Complex network · Data mining · Economics · Eigenvalues and eigenvectors · Measure (data warehouse · Metric (unit · Statistics · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Social Capital and Networks · Theoretical Computer Science

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Unique citing works10
Citations per year2,5
Citation span2022 - 2026 (5)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 10

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