A tensor-based framework for studying eigenvector multicentrality in multilayer networks

Mincheng Wu, Shibo He, Yongtao Zhang, Jiming Chen, Youxian Sun, Yang Yu Liu, Junshan Zhang, H. Vincent Poor

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Centrality is widely recognized as one of the most critical measures to provide insight into the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks a general framework for studying centrality in multilayer networks (i.e. multicentrality) is still lacking. In this study a tensor-based framework is introduced to study eigenvector multicentrality which enables the quantification of the impact of interlayer influence on multicentrality providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.

Original languageEnglish (US)
Pages (from-to)15407-15413
Number of pages7
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number31
DOIs
StatePublished - Jul 30 2019

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Eigenvector centrality
  • Multilayer networks
  • PageRank centrality

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