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Bridges in complex networks.

Ang-Kun Wu1,2,3, Liang Tian1,4, Yang-Yu Liu1,5

  • 1Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.

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|February 17, 2018
PubMed
Summary
This summary is machine-generated.

Real-world networks have more bridges than random networks, with fractions similar to degree-preserving randomizations. A new metric, bridgeness, quantifies bridge importance in network damage.

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Area of Science:

  • Graph theory
  • Network science
  • Complex systems analysis

Background:

  • Bridges are critical edges in graph theory, identified by their role in disconnecting networks upon removal.
  • Understanding bridge prevalence is crucial for network robustness and analysis.

Purpose of the Study:

  • To quantify the fraction of bridges in real-world networks compared to randomized models.
  • To introduce and analyze 'bridgeness,' a novel edge centrality measure for network damage assessment.
  • To develop an analytical framework for bridge fraction and bridgeness in random networks.

Main Methods:

  • Calculation of bridge fraction across diverse real-world networks and their randomized counterparts.
  • Definition and computation of bridgeness as an edge centrality measure.
  • Development of an analytical framework for bridge properties in uncorrelated random networks.

Main Results:

  • Real networks exhibit a higher bridge fraction than completely randomized networks.
  • Bridge fractions in real networks closely resemble those in degree-preserving randomizations.
  • Certain real networks show significantly higher average and variance in bridgeness compared to randomized networks.

Conclusions:

  • Degree-preserving randomization is a more relevant null model for bridge analysis than complete randomization.
  • Bridgeness offers a valuable metric for identifying critical edges and understanding network vulnerability.
  • The analytical framework provides theoretical insights into bridge properties in complex networks.