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Marina Diakonova1, Víctor M Eguíluz1, Maxi San Miguel1

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This summary is machine-generated.

Network dynamics and topology changes cause absorbing and fragmentation transitions. Noise impacts these transitions differently: homogeneous noise destroys them, while targeted noise preserves them but alters fragmentation patterns.

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Area of Science:

  • Complex systems
  • Network science
  • Statistical physics

Background:

  • Coupling network state dynamics with topology changes can induce absorbing and fragmentation transitions.
  • The coevolving voter model serves as a standard framework for studying these phenomena at critical rewiring thresholds.

Purpose of the Study:

  • To investigate the robustness of absorbing and fragmentation transitions in coevolving networks under different noise conditions.
  • To analyze the impact of homogeneous versus targeted noise on network transition dynamics and fragmentation patterns.

Main Methods:

  • Simulating the coevolving voter model with two distinct noise injection strategies: homogeneous (affecting all nodes) and targeted (affecting a fraction of nodes).
  • Analyzing system behavior, including magnetization and fragmentation characteristics, in finite-size systems under varying noise levels and rewiring rates.
  • Employing analytical approximations to support observed transition behaviors.

Main Results:

  • Homogeneous noise disrupts the absorbing-fragmentation transition, leading to bimodal magnetization and dynamic fragmentation in finite systems.
  • Targeted noise preserves the transitions but results in shattered fragmentation, characterized by isolated nodes and one or two giant components.
  • Analytical approximations confirm the absence of an absorbing state under homogeneous noise and a shift in the transition point for targeted noise.

Conclusions:

  • The nature of noise critically influences the absorbing and fragmentation transitions in coevolving networks.
  • Targeted noise introduces a novel fragmented state, distinct from the behavior observed under homogeneous noise or in noise-free systems.
  • Understanding these noise-induced effects is crucial for predicting network behavior in realistic, noisy environments.