Abstract
We derive and analyse a new iterative algorithm for detecting network core–periphery structure.
Using techniques in nonlinear Perron-Frobenius theory, we prove global convergence to the unique solution of a relaxed version of a natural discrete optimization problem.
On sparse networks, the cost of each iteration scales linearly with the number of nodes, making the algorithm feasible for large-scale problems. We give an alternative interpretation of the algorithm from the perspective of maximum likelihood reordering of a new logistic core–periphery
random graph model. This viewpoint also gives a new basis for quantitatively
judging a core–periphery detection algorithm. We illustrate the algorithm on a range of synthetic and real networks, and show that it offers advantages over the current state-of-the-art.
Please cite this paper as:
@article{tudisco2018core,
title = {A nonlinear spectral method for core-periphery detection in networks},
author = {Tudisco, Francesco and Higham, Desmond J.},
journal = {SIAM J. Mathematics of Data Science},
volume = {1},
pages = {269-292},
year = {2019}
}