The seformed graph laplacian and its applications to network centrality analysis

Author: 

Grindrod, P
Highham, D
Noferini, V

Publication Date: 

1 March 2018

Journal: 

SIAM Journal on Matrix Analysis and Applications

Last Updated: 

2020-07-10T12:46:44.9+01:00

Issue: 

1

Volume: 

39

DOI: 

10.1137/17M1112297

page: 

310–341-

abstract: 

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.

Symplectic id: 

796049

Submitted to ORA: 

Submitted

Publication Type: 

Journal Article