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Robustness and efficiency in interconnected networks with changes in network assortativity.

Masaya Murakami1, Shu Ishikura1, Daichi Kominami2

  • 11Department of Information Science and Technology, Osaka University, 1-5 Yamada-oka, Suita, Osaka, 560-0871 Japan.

Applied Network Science
|December 12, 2018
PubMed
Summary
This summary is machine-generated.

Decreasing assortativity in networks improves efficiency and load distribution. However, excessive assortativity harms performance, while inter-network assortativity impacts hop length and robustness, requiring careful adjustment for optimal network design.

Keywords:
AssortativityBrain networkGraph theoryInterconnected networkInternet of things (IoT)Modular structure

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Area of Science:

  • Network Science
  • Complex Systems

Background:

  • Assortativity, a measure of network's tendency to connect similar nodes, influences network properties.
  • Understanding assortativity's impact is crucial for designing efficient and robust interconnected systems.

Purpose of the Study:

  • To investigate the effect of assortativity on the robustness and efficiency of single and interconnected networks.
  • To determine optimal assortativity levels for network performance.

Main Methods:

  • Constructed single networks with controlled assortativity.
  • Developed interconnected networks with specified inter-component assortativity.
  • Analyzed network performance metrics like hop length, information diffusion, and load distribution.

Main Results:

  • Lower assortativity in single networks reduces hop length and enhances information diffusion efficiency.
  • Excessive assortativity negatively impacts single network performance.
  • Assortative inter-network connections decrease average hop length and improve diffusion.
  • Disassortative inter-network connections enhance robustness and distribute communication loads.

Conclusions:

  • Assortativity significantly affects network efficiency and robustness.
  • Careful adjustment of assortativity, considering node degree distribution, is essential for designing effective information networks.
  • Findings offer insights for optimizing the architecture of interconnected systems.