Communicability across evolving networks

Peter Grindrod, Mark C. Parsons, D.J. Higham, Ernesto Estrada

Research output: Contribution to journalArticle

117 Citations (Scopus)

Abstract

Many natural and technological applications generate time-ordered sequences of networks, defined over a fixed set of nodes; for example, time-stamped information about “who phoned who” or “who came into contact with who” arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time’s arrow is captured naturally through the noncommutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction.

Original languageEnglish
Pages (from-to)Article 046120
Number of pages10
JournalPhysical Review E
Volume83
Issue number4
DOIs
Publication statusPublished - 25 Apr 2011

Keywords

  • complex networks
  • graphs
  • moble
  • flow
  • centrality

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