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Title: Informative core identification in complex networks
Abstract In a complex network, the core component with interesting structures is usually hidden within noninformative connections. The noises and bias introduced by the noninformative component can obscure the salient structure and limit many network modeling procedures’ effectiveness. This paper introduces a novel core–periphery model for the noninformative periphery structure of networks without imposing a specific form of the core. We propose spectral algorithms for core identification for general downstream network analysis tasks under the model. The algorithms enjoy strong performance guarantees and are scalable for large networks. We evaluate the methods by extensive simulation studies demonstrating advantages over multiple traditional core–periphery methods. The methods are also used to extract the core structure from a citation network, which results in a more interpretable hierarchical community detection.  more » « less
Award ID(s):
2015298
PAR ID:
10431304
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Journal of the Royal Statistical Society Series B: Statistical Methodology
Volume:
85
Issue:
1
ISSN:
1369-7412
Page Range / eLocation ID:
108 to 126
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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