Eigenvector centrality of nodes in multiplex networks
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Abstract (arXiv)
We extend the concept of eigenvector centrality to multiplex networks, and introduce several alternative parameters that quantify the importance of nodes in a multi-layered networked system, including the definition of vectorial-type centralities. In addition, we rigorously show that, under reasonable conditions, such centrality measures exist and are unique. Computer experiments and simulations demonstrate that the proposed measures provide substantially different results when applied to the same multiplex structure, and highlight the non-trivial relationships between the different measures of centrality introduced.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2013.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 1.
6 papers.
- 2022Convolutional Learning on Multigraphscited by 10
- 2024AI-enhanced Collective Intelligencecited by 10
- 2021mGNN: Generalizing the Graph Neural Networks to the Multilayer Casecited by 4
- 2025Effective Fine-Tuning with Eigenvector Centrality Based Pruningcited by 1
- 2021A Tensor-Based Formulation of Hetero-functional Graph Theorycited by 0
- 2023DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networkscited by 0
- 2025Effective Fine-Tuning with Eigenvector Centrality Based Pruning14 Dec 2025
- 2024AI-enhanced Collective Intelligence15 Mar 2024
- 2023DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks8 Feb 2023
- 2022Convolutional Learning on Multigraphs23 Sep 2022
- 2021mGNN: Generalizing the Graph Neural Networks to the Multilayer Case21 Sep 2021
- 2021A Tensor-Based Formulation of Hetero-functional Graph Theory14 Jan 2021