Eigenvector centrality of nodes in multiplex networks

TL;DR

This paper generalizes eigenvector centrality to multiplex networks, introducing new measures that account for multi-layered structures, with theoretical guarantees and empirical comparisons showing their distinct insights.

Contribution

It develops a rigorous framework for eigenvector centrality in multiplex networks, including new vectorial centrality measures and proofs of their existence and uniqueness.

Findings

Proposed centrality measures differ significantly in multiplex networks.

Empirical results highlight non-trivial relationships between different centrality measures.

The measures provide new insights into node importance in multi-layered systems.

Abstract

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.