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Towards Optimal Connectivity on Multi-layered Networks.

Chen Chen1, Jingrui He1, Nadya Bliss1

  • 1Arizona State University, Tempe, AZ, 85281, USA.

IEEE Transactions on Knowledge and Data Engineering
|May 15, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces SUBLINE, a new set of connectivity measures for multi-layered networks. SUBLINE offers efficient optimization for complex systems vulnerable to cascading failures.

Keywords:
Multi-layered NetworksNetwork Connectivity

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Data Science

Background:

  • Multi-layered networks, comprising interconnected systems, are crucial in domains like critical infrastructure and biology.
  • These networks are susceptible to cascading failures due to amplified cross-layer dependencies.
  • Existing methods for manipulating multi-layered network connectivity are limited.

Purpose of the Study:

  • To develop a unified framework for connectivity measures in multi-layered networks.
  • To address the vulnerability of these networks to disturbances and cascading failures.
  • To propose an efficient algorithm for optimizing network connectivity.

Main Methods:

  • Introduction of the SUBLINE family of connectivity measures, unifying existing approaches.
  • Demonstration of the diminishing returns property within the SUBLINE family.
  • Development of a linear-complexity algorithm for connectivity optimization.

Main Results:

  • The SUBLINE family provides a versatile and unified approach to network connectivity.
  • The diminishing returns property ensures near-optimal solutions efficiently.
  • The proposed algorithm shows effectiveness and efficiency on real-world datasets.

Conclusions:

  • SUBLINE offers a powerful new tool for analyzing and optimizing multi-layered networks.
  • The diminishing returns property is key to efficient connectivity optimization.
  • The findings have implications for understanding and mitigating failures in complex interconnected systems.