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

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
JournalJournal of Social Structure
Volume18
DOIs
StatePublished - 2017

Keywords

  • Centrality
  • Eigenvector centrality
  • Social networks

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