Eigenvector centralization as a measure of structural bias in information aggregation

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. © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.