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Eigenvector centrality for multilayer networks with dependent node importance.

H Robert Frost1

  • 1Dartmouth College, Hanover NH 03755, USA.

Complex Networks & Their Applications. International Conference on Complex Networks and Their Applications
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new method for calculating eigenvector centrality in multilayer networks, considering constraints between node importance across different layers. This approach efficiently computes layer-specific centrality values for complex network analysis.

Keywords:
eigenvector centralitymultilayer networks

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Area of Science:

  • Network Science
  • Graph Theory
  • Computational Social Science

Background:

  • Eigenvector centrality is a key metric for identifying influential nodes in networks.
  • Multilayer networks, composed of interconnected layers, require specialized centrality measures.
  • Existing methods for multilayer eigenvector centrality have limitations in handling inter-layer constraints.

Purpose of the Study:

  • To introduce a novel approach for computing eigenvector centrality in multilayer networks.
  • To address inter-layer constraints on node importance.
  • To provide an efficient computational method for these complex networks.

Main Methods:

  • Defined a multilayer network model with distinct layers and inter-layer constraints on node importance.
  • Formulated layer-specific eigenvector centrality using independent and dependent pseudo-eigenvalue problems.
  • Developed an interleaved power iteration algorithm for efficient computation.

Main Results:

  • The proposed method effectively computes constrained, layer-specific eigenvector centrality values.
  • The interleaved power iteration algorithm provides an efficient solution.
  • An R package is available for practical implementation.

Conclusions:

  • This novel approach extends eigenvector centrality to multilayer networks with inter-layer constraints.
  • The computational method is efficient and practical for complex network analysis.
  • The R package facilitates the application of this method in research.