ArticleProceedings of the National Academy of Sciences of the United States of America2019
A tensor-based framework for studying eigenvector multicentrality in multilayer networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Towards a hierarchical approach and scalable semi-local centrality for key node identification in weighted complex networks.Scientific reports · 2026Article
- TransMarker: Unveiling dynamic network biomarkers in cancer progression through cross-state graph alignment and optimal transport.PLoS computational biology · 2025Article
- Discrimination reveals reconstructability of multiplex networks from partial observations.Communications physics · 2022Article
- Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data.Communications physics · 2022Article
- Topological clustering of multilayer networks.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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Authors and funding
8 authors.
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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.
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