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Growing multiplex networks.

V Nicosia1, G Bianconi, V Latora

  • 1School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E1 4NS London, United Kingdom.

Physical Review Letters
|August 20, 2013
PubMed
Summary
This summary is machine-generated.

We developed a new framework to model growing complex networks (multiplexes) where nodes join multiple networks. This model reveals how node arrival times and network coupling impact network structure and interdependence.

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Area of Science:

  • Complex systems
  • Network science
  • Mathematical modeling

Background:

  • Multiplex networks, where nodes belong to multiple interconnected networks, are prevalent in real-world systems.
  • Understanding the dynamics of growing multiplexes is crucial for predicting their structural evolution and emergent properties.

Purpose of the Study:

  • To propose a novel modeling framework for the evolution of growing multiplexes.
  • To introduce new measures for characterizing multiplex network properties.
  • To identify key factors influencing multiplex topology and interdependence.

Main Methods:

  • Development of a computational framework for simulating growing multiplexes.
  • Definition of new topological and interdependence measures for multilayer networks.
  • Analysis of the impact of node arrival time distributions and inter-layer coupling on network evolution.

Main Results:

  • The proposed framework successfully models growing multiplexes with varying node arrival times and coupling strengths.
  • Node arrival times significantly affect key network metrics like degree distribution and average shortest path length.
  • Interdependence between network layers is shown to be a critical factor in multiplex topology evolution.

Conclusions:

  • The developed modeling framework provides valuable insights into the growth dynamics of complex multiplex networks.
  • Node arrival patterns and inter-layer coupling are essential determinants of multiplex network structure and function.
  • The study highlights the importance of considering temporal aspects and layer interactions in network science.