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Effect of Inter-layer Coupling on Multilayer Network Centrality Measures.

Tarun Kumar1,2, Manikandan Narayanan1,2, Balaraman Ravindran1,2

  • 1Robert Bosch Centre for Data Science and AI (RBCDSAI), Chennai, India.

Journal of the Indian Institute of Science
|July 20, 2021
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Summary
This summary is machine-generated.

Understanding node importance in multilayer networks is key. This study reviews centrality measures, focusing on how inter-layer coupling affects node importance in complex systems.

Keywords:
Betweenness centralityEigenvector centralityInterconnected networksMultilayer networksMultiplex networksPageRank centrality

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Area of Science:

  • Network Science
  • Complex Systems Analysis

Background:

  • Multilayer networks model complex systems with diverse interactions.
  • Node centrality is crucial for understanding network importance.
  • Extending centrality to multilayer networks presents challenges due to inter-layer coupling.

Purpose of the Study:

  • To provide an overview of recent centrality measures in multilayer networks.
  • To analyze the impact of inter-layer coupling on centrality.
  • To discuss implications for network analysis and applications.

Main Methods:

  • Review of existing literature on multilayer network centrality.
  • Discussion of three popular inter-layer coupling methods: diagonal coupling (adjacent layers), diagonal coupling, and cross-coupling.
  • Analysis of theoretical and experimental aspects.

Main Results:

  • Different inter-layer coupling methods yield distinct centrality measures.
  • The choice of coupling significantly influences node importance rankings.
  • Understanding these effects is vital for accurate network analysis.

Conclusions:

  • Centrality in multilayer networks is sensitive to inter-layer coupling strategies.
  • Careful consideration of coupling is necessary for reliable network analysis.
  • This work aids in selecting appropriate centrality measures for specific applications.