# Eigenvector centrality of nodes in multiplex networks

arXiv: https://arxiv.org/abs/1305.7445 · HTML: https://mlsanity.com/paper/1305.7445/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2013.

## Built on by (most cited first, 6 of 6)

- [Convolutional Learning on Multigraphs](https://mlsanity.com/paper/2209.11354/) (2022)
- [AI-enhanced Collective Intelligence](https://mlsanity.com/paper/2403.10433/) (2024)
- [mGNN: Generalizing the Graph Neural Networks to the Multilayer Case](https://mlsanity.com/paper/2109.10119/) (2021)
- [Effective Fine-Tuning with Eigenvector Centrality Based Pruning](https://mlsanity.com/paper/2512.12543/) (2025)
- [A Tensor-Based Formulation of Hetero-functional Graph Theory](https://mlsanity.com/paper/2101.07220/) (2021)
- [DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks](https://mlsanity.com/paper/2302.03907/) (2023)

## 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.