The deformed graph Laplacian and its applications to network centrality analysis

Peter Grindrod, Desmond J. Higham, Vanni Noferini

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We introduce and study a new network centrality measure based on the concept of nonbacktracking walks; that is, walks not containing subsequences of the form uvu where u and v are any distinct connected vertices of the underlying graph. We argue that this feature can yield more meaningful rankings than traditional walk-based centrality measures. We show that the resulting Katz-style centrality measure may be computed via the so-called deformed graph Laplacian—a quadratic matrix polynomial that can be associated with any graph. By proving a range of new results about this matrix polynomial, we gain insights into the behavior of the algorithm with respect to its Katz-like parameter. The results also inform implementation issues. In particular we show that, in an appropriate limit, the new measure coincides with the nonbacktracking version of eigenvector centrality introduced by Martin, Zhang and Newman in 2014. Rigorous analysis on star and star-like networks illustrates the benefits of the new approach, and further results are given on real networks.
Original languageEnglish
Pages (from-to)310-341
Number of pages32
JournalSIAM Journal on Matrix Analysis and Applications
Issue number1
Publication statusPublished - 1 Mar 2018

Keywords / Materials (for Non-textual outputs)

  • centrality index
  • deformed graph Laplacian
  • matrix polynomial
  • nonbacktracking
  • complex networks
  • generating function


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