Finding core–periphery structures in large networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number126224
Journal / PublicationPhysica A: Statistical Mechanics and its Applications
Volume581
Online published3 Jul 2021
Publication statusPublished - 1 Nov 2021

Abstract

Finding core–periphery structures in networks is very useful in many disciplines such as biology and sociology. However, most of the previous works focus on the single core–periphery structure in the network. A few recent algorithms considering multiple core–periphery are usually not suitable for large networks. Inspired by the modularity maximization method for community detection, we propose a simple but effective approach to detect core–periphery structures in this work. Moreover, we propose a metric called core–periphery score to evaluate the performance of core–periphery structure detection algorithms. In the experiment, we find that the score is consistent with the normalized mutual information when ground-truth structures are given. Our approach also outperforms other core–periphery detection algorithms for randomly generated networks and real-world networks.

Research Area(s)

  • Core–periphery score, Core–periphery structure detection, Stochastic blockmodels

Citation Format(s)

Finding core–periphery structures in large networks. / Shen, Xin; Han, Yue; Li, Wenqian et al.

In: Physica A: Statistical Mechanics and its Applications, Vol. 581, 126224, 01.11.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review