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


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Journal of Social Structure

International Network for Social Network Analysis

Subject: Social Sciences


eISSN: 1529-1227





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VOLUME 18 , ISSUE 1 (December 2017) > List of articles

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

Dawn Iacobucci * / Rebecca McBride / Deidre L. Popovich

Keywords : centrality, eigenvector centrality, social networks

Citation Information : Journal of Social Structure. Volume 18, Issue 1, Pages 0-0, DOI: https://doi.org/10.21307/joss-2018-003

License : (CC BY 4.0)

Published Online: 11-March-2018



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.

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