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.
Original language | English |
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Article number | 1 |
Pages (from-to) | 269-292 |
Number of pages | 24 |
Journal | SIAM Journal on the Mathematics of Data Science (SIMODS) |
Volume | 1 |
Issue number | 2 |
DOIs | |
Publication status | Published - 11 Apr 2019 |
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Desmond Higham
- School of Mathematics - Professor of Numerical Analysis
Person: Academic: Research Active (Teaching)