A novel measure of edge centrality in social networks

  • Authors:

  • Pasquale De Meo;Emilio Ferrara;Giacomo Fiumara;Angela Ricciardello

  • Affiliations:

  • Department of Physics, Informatics Section, University of Messina, V.le F. Stagno D'Alcontres 31, 98166 ME, Italy;Department of Mathematics, University of Messina, V.le F. Stagno D'Alcontres 31, 98166 ME, Italy;Department of Physics, Informatics Section, University of Messina, V.le F. Stagno D'Alcontres 31, 98166 ME, Italy;Department of Mathematics, University of Messina, V.le F. Stagno D'Alcontres 31, 98166 ME, Italy

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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Abstract

The problem of assigning centrality values to nodes and edges in graphs has been widely investigated during last years. Recently, a novel measure of node centrality has been proposed, called @k-path centrality index, which is based on the propagation of messages inside a network along paths consisting of at most @k edges. On the other hand, the importance of computing the centrality of edges has been put into evidence since 1970s by Anthonisse and, subsequently by Girvan and Newman. In this work we propose the generalization of the concept of @k-path centrality by defining the @k-path edge centrality, a measure of centrality introduced to compute the importance of edges. We provide an efficient algorithm, running in O(@km), being m the number of edges in the graph. Thus, our technique is feasible for large scale network analysis. Finally, the performance of our algorithm is analyzed, discussing the results obtained against large online social network datasets.