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Eigenvector Centrality: Illustrations Supporting the Utility of Extracting More Than One Eigenvector to Obtain Additional Insights into Networks and Interdependent Structures

Journal of Social Structure, 18 (2), 1-22, 2017

22 Pages Posted: 11 Oct 2017 Last revised: 18 Nov 2017

Dawn Iacobucci

Vanderbilt University - Marketing

Rebecca S McBride

Calvin College

Deidre Popovich

Rawls College of Business, Texas Tech University

Date Written: October 1, 2017

Abstract

Among the many centrality indices used to detect structures of actors’ positions in networks is the use of the first eigenvector of an adjacency matrix that captures the connections among the actors. This research considers the seeming pervasive current practice of using only the first eigenvector. It is shows that, as in other statistical applications of eigenvectors, subsequent vectors can also contain illuminating information. Several small examples, and Freeman’s EIES network, are used to illustrate that while the first eigenvector is certainly informative, the second (and subsequent) eigenvector(s) can also be equally tractable and informative.

Keywords: centrality, eigenvector centrality, social networks

Suggested Citation

Iacobucci, Dawn and McBride, Rebecca S and Popovich, Deidre, Eigenvector Centrality: Illustrations Supporting the Utility of Extracting More Than One Eigenvector to Obtain Additional Insights into Networks and Interdependent Structures (October 1, 2017). Journal of Social Structure, 18 (2), 1-22, 2017. Available at SSRN: https://ssrn.com/abstract=3051421

Dawn Iacobucci (Contact Author)

Vanderbilt University - Marketing ( email )

Nashville, TN 37203
United States

Rebecca McBride

Calvin College ( email )

Grand Rapids, MI 49546
United States

Deidre Popovich

Rawls College of Business, Texas Tech University ( email )

Lubbock, TX 79409
United States

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