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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
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Cascades Towards Noise-Induced Transitions on Networks Revealed Using Information Flows.

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Area of Science:

  • Complex Systems Science
  • Network Dynamics
  • Statistical Physics

Background:

  • Complex networks exhibit spontaneous, system-wide transitions without external triggers, termed noise-induced transitions.
  • The precise mechanisms underlying these endogenous transitions, driven by network structure and local dynamics, remain poorly understood.

Purpose of the Study:

  • To elucidate the critical roles of individual nodes in catalyzing endogenous transitions within dynamical complex networks.
  • To introduce and define

Main Methods:

  • Investigated dynamical networks governed by the Boltzmann-Gibbs distribution.
  • Introduced and analyzed the roles of

Main Results:

  • Identified "initiator nodes" that absorb fluctuations, destabilize neighbors, and trigger a domino effect.
  • Discovered "stabilizer nodes" encoding long-term memory that reverse the domino effect, leading to new stable states.

Conclusions:

  • Nodes play dual, critical roles in initiating and stabilizing complex network transitions.
  • Findings offer a framework for understanding and controlling metastable behavior in diverse systems, from neuroscience to social dynamics.