About this Journal Submit a Manuscript Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 871038, 10 pages
http://dx.doi.org/10.1155/2014/871038
Research Article

Balanced Centrality of Networks

Department of Mathematics, Faculty of Science, University of Malta Msida, MSD 2080, Malta

Received 28 March 2014; Accepted 13 July 2014; Published 3 November 2014

Academic Editor: Yongtang Shi

Copyright © 2014 Mark Debono et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There is an age-old question in all branches of network analysis. What makes an actor in a network important, courted, or sought? Both Crossley and Bonacich contend that rather than its intrinsic wealth or value, an actor’s status lies in the structures of its interactions with other actors. Since pairwise relation data in a network can be stored in a two-dimensional array or matrix, graph theory and linear algebra lend themselves as great tools to gauge the centrality (interpreted as importance, power, or popularity, depending on the purpose of the network) of each actor. We express known and new centralities in terms of only two matrices associated with the network. We show that derivations of these expressions can be handled exclusively through the main eigenvectors (not orthogonal to the all-one vector) associated with the adjacency matrix. We also propose a centrality vector (SWIPD) which is a linear combination of the square, walk, power, and degree centrality vectors with weightings of the various centralities depending on the purpose of the network. By comparing actors’ scores for various weightings, a clear understanding of which actors are most central is obtained. Moreover, for threshold networks, the (SWIPD) measure turns out to be independent of the weightings.