A tensor-based framework for studying eigenvector multicentrality in multilayer networks
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 17, 2019
- Source ID
- 10.1073/pnas.1801378116
Entities
People
- Jiming Chen
- Junshan Zhang
- Mincheng Wu
- Shibo He
- Vincent Poor
- Yang-Yu Liu
- Yongtao Zhang
- Youxian Sun
Organizations
- Arizona State University
- Army Research Office
- Dana–Farber Cancer Institute
- Defense Threat Reduction Agency
- Harvard Medical School
- Princeton University
- Zhejiang University