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Cascading failures in spatially-embedded random networks.

Andrea Asztalos1, Sameet Sreenivasan1, Boleslaw K Szymanski2

  • 1Social and Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy, New York, United States of America ; Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America ; Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, New York, United States of America.

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Cascading failures on spatial networks are non-self-averaging, meaning large-scale network analysis doesn't apply to individual systems. Adding long-range links improves reliability and mitigation strategies for these critical infrastructures.

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Area of Science:

  • Complex systems science
  • Network science
  • Statistical physics

Background:

  • Interconnected systems are vulnerable to cascading failures.
  • Geographical constraints influence network connectivity.
  • Spatial networks, like random geometric graphs, model real-world systems.

Purpose of the Study:

  • Investigate cascading failures in spatially constrained networks.
  • Analyze the self-averaging properties of these failures.
  • Evaluate cascade mitigation strategies.

Main Methods:

  • Utilized random geometric graphs to represent spatial networks.
  • Simulated cascading failures with distributed flow.
  • Introduced and tested preemptive and altruistic node removal strategies.
  • Validated findings on a European power transmission network.

Main Results:

  • Cascading failures are non-self-averaging on spatial networks.
  • Aggregate network analysis yields incorrect conclusions for individual networks.
  • Adding a small fraction of long-range links restores self-averaging.
  • Preemptive node removal is ineffective; altruistic strategy performs better.
  • Findings are consistent with real-world power grid simulations.

Conclusions:

  • Spatial network structure significantly impacts cascading failure dynamics.
  • Network mitigation strategies must account for spatial properties.
  • Altruistic strategies offer a more effective approach to cascade control in spatial networks.