A divide‐and‐conquer algorithm for core‐periphery identification in large networks. Issue 1 (7th October 2022)

• Record Type: Journal Article
• Authors: Yanchenko, Eric
• Is Part Of: Stat. Volume 11:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-07
• Subjects: large and complex data sets -- networks -- statistical computing; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.475
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : Core‐periphery structure is an important network feature where the network is broken into two components: a densely connected core and a loosely connected periphery. In this work, we propose a divide‐and‐conquer algorithm to identify the core‐periphery structure in large networks. By finding this structure on much smaller sub‐samples of the network and then combining the results across sub‐samples, this method yields fast and accurate core‐periphery labels. Additionally, the method provides a measure of the statistical significance of the structure. We apply our approach to synthetic data to find the algorithm's detection limit and on a real‐world network with more than 35, 000 nodes.