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

Author(s): Wu, Mincheng; He, Shibo; Zhang, Yongtao; Chen, Jiming; Sun, Youxian; et al

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Abstract: It is of significant interest to understand the structure and function of multilayer networks, which model many practical complex systems. Centrality, quantifying the importance of nodes in a graph, is widely recognized as one of the most effective measures. Nevertheless, a general framework for characterizing centrality in multilayer networks is still lacking. In this article, we fill this gap by developing a tensor-based framework for characterizing eigenvector multicentrality in general multilayer networks. We prove the existence and uniqueness of eigenvector multicentrality for 2 interesting scenarios, using the proposed framework. The results from empirical networks demonstrate that this framework helps us obtain a clear understanding of the eigenvector multicentrality of nodes.
Publication Date: 19-Jul-2019
Citation: Wu, Mincheng, He, Shibo, Zhang, Yongtao, Chen, Jiming, Sun, Youxian, Liu, Yang-Yu, Zhang, Junshan, Poor, H Vincent. (2019). A tensor-based framework for studying eigenvector multicentrality in multilayer networks. Proceedings of the National Academy of Sciences, 116 (31), 15407 - 15413. doi:10.1073/pnas.1801378116
DOI: doi:10.1073/pnas.1801378116
ISSN: 0027-8424
EISSN: 1091-6490
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Proceedings of the National Academy of Sciences
Version: Author's manuscript

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