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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Algorithm to determine the percolation largest component in interconnected networks.

Christian M Schneider1, Nuno A M Araújo, Hans J Herrmann

  • 1Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. schnechr@gmail.com

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

We developed an efficient algorithm to identify the largest cluster in interconnected networks, improving resilience analysis. This method significantly speeds up the study of network failures and risk mitigation strategies.

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Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Interconnected networks are more susceptible to failures than isolated ones.
  • Assessing network resilience requires understanding failure impacts and risk mitigation.
  • The percolation model quantifies resilience by analyzing the largest cluster size post-failure.

Purpose of the Study:

  • To propose an efficient algorithm for identifying the largest cluster in interconnected networks.
  • To improve the numerical identification and size calculation of the largest cluster.
  • To enable the study of larger networks and complex failure scenarios.

Main Methods:

  • Developed a novel algorithm for cluster identification in networks.
  • Analyzed the computational complexity of the proposed algorithm.
  • Compared the algorithm's performance against existing greedy approaches.

Main Results:

  • The proposed algorithm achieves a time complexity of O(NlogN), where N is the number of nodes.
  • This represents a significant improvement over the O(N^2) complexity of greedy algorithms.
  • The algorithm efficiently identifies the largest cluster and its size, crucial for resilience assessment.

Conclusions:

  • The new algorithm enables the study of much larger networks and complex failure patterns.
  • It offers a scalable solution for identifying critical network components and assessing systemic risk.
  • The method is applicable to diverse network topologies and failure sequences, enhancing network resilience research.