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In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Network dismantling.

Alfredo Braunstein1,2,3, Luca Dall'Asta1,3, Guilhem Semerjian4

  • 1Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.

Proceedings of the National Academy of Sciences of the United States of America
|November 3, 2016
PubMed
Summary
This summary is machine-generated.

We identify minimal vertex sets to break networks. A new algorithm efficiently dismantles networks, revealing collective behavior in optimal dismantling strategies.

Keywords:
graph fragmentationinfluence maximizationmessage passingpercolationrandom graphs

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Area of Science:

  • Network science
  • Statistical physics
  • Graph theory

Background:

  • The network dismantling problem seeks minimal vertex sets to fragment networks.
  • This problem is linked to graph decycling, especially in random graphs.

Purpose of the Study:

  • To predict the size of dismantling sets in random graphs.
  • To develop an efficient algorithm for network dismantling, particularly for heavy-tailed networks.

Main Methods:

  • Connecting network dismantling to decycling and epidemic spreading models.
  • Utilizing a statistical mechanics perspective.
  • Developing a three-stage Min-Sum algorithm.

Main Results:

  • Precise predictions for dismantling set sizes in random graphs with light-tailed distributions.
  • An efficient algorithm applicable to heavy-tailed networks.
  • Demonstration that dismantling is a collective problem, not reducible to individual node performance.

Conclusions:

  • Network dismantling is fundamentally a collective phenomenon.
  • Optimal dismantling sets are not merely collections of individually effective nodes.