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Quantifying layer similarity in multiplex networks: a systematic study.

Piotr Bródka1, Anna Chmiel2, Matteo Magnani3

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Summary
This summary is machine-generated.

Comparing layers in multiplex networks is crucial for understanding network behavior. This study offers a comprehensive taxonomy and practical guidelines for evaluating layer similarity approaches.

Keywords:
layer similaritymultiplex networksnetwork similarityproperty matrix

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Area of Science:

  • Network Science
  • Data Analysis

Background:

  • Multiplex networks, comprising multiple layers of interconnected nodes, are prevalent in various systems.
  • Characterizing relationships between these layers is essential for understanding network properties and dynamics.

Purpose of the Study:

  • To provide a systematic taxonomy of methods for computing layer similarities in multiplex networks.
  • To experimentally evaluate existing and novel approaches for layer comparison.
  • To offer practical guidelines for selecting and applying appropriate comparison methods.

Main Methods:

  • Development of a comprehensive taxonomy categorizing layer similarity computation techniques.
  • Experimental evaluation of different similarity metrics across diverse multiplex network structures.
  • Analysis of the performance and applicability of various approaches.

Main Results:

  • The study categorizes and extends existing layer similarity methods.
  • Experimental results provide insights into the effectiveness of different approaches under various conditions.
  • Practical guidelines are established for researchers and practitioners.

Conclusions:

  • A structured framework for understanding and applying layer similarity computations in multiplex networks is presented.
  • The findings facilitate more accurate characterization and analysis of complex multiplex systems.
  • This work enhances the ability to study static and dynamic processes within multiplex networks.