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Area of Science:

  • Network Science
  • Statistical Physics
  • Complex Systems

Background:

  • Random walks are crucial for modeling transport in complex networks.
  • Stochastic resetting optimizes search times but its impact on extreme events is understudied.
  • Extreme events, like traffic congestion or server overloads, are critical network phenomena.

Purpose of the Study:

  • To investigate the influence of stochastic resetting on the probability and magnitude of extreme events in complex networks.
  • To understand how resetting affects critical network phenomena such as congestion and failures.
  • To explore the potential of resetting as a control mechanism for mitigating extreme events.

Main Methods:

  • Analytical derivation of stationary occupation probabilities.
  • Comprehensive numerical simulations of random walks with stochastic resetting.
  • Systematic analysis of extreme event occurrence and magnitude across network nodes.

Main Results:

  • Stochastic resetting significantly reduces the probability of extreme events.
  • Resetting concentrates event-size fluctuations rather than eliminating them.
  • A universal suppression effect was observed, with increasing resetting rates monotonically decreasing extreme event probabilities.
  • Low-degree nodes and those distant from the resetting point benefit most from suppression.

Conclusions:

  • Stochastic resetting is an effective strategy for mitigating extreme events in complex networks.
  • The findings provide theoretical underpinnings for utilizing resetting as a control mechanism.
  • Resetting offers a tunable method to manage critical phenomena in networked systems.