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Emergent information dynamics in many-body interconnected systems.

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Summary

This study introduces a new mathematical framework to model information flow on complex networks. It reveals hidden network dynamics using quantum-inspired methods, applicable to diverse systems like epidemics and social contagion.

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Area of Science:

  • Complex Systems Science
  • Statistical Mechanics
  • Information Theory

Background:

  • Information in physical systems is analyzed using statistical mechanics and information theory.
  • This approach has been applied to complex networks like protein interactions and brain models, inspired by quantum statistical physics.

Purpose of the Study:

  • To propose a general mathematical framework for modeling information dynamics on complex networks.
  • To enable nodes to carry multiple types of information using vector-valued states.
  • To shift focus from node-node interactions to information flow between network configurations.

Main Methods:

  • Developing a general mathematical framework for information dynamics on complex networks.
  • Utilizing vector-valued node states to represent multiple information types.
  • Analyzing information flow between network configurations, inspired by quantum many-body systems.

Main Results:

  • Uncovered fundamental differences in network spin models (e.g., voter, kinetic dynamics) undetectable by classical analysis.
  • Demonstrated the framework's applicability to epidemic spreading on a low-dimensional network.
  • Provided a method to adapt quantum many-body system analytical techniques to network dynamics.

Conclusions:

  • The proposed framework offers a novel approach to understanding information dynamics in complex systems.
  • It allows for deeper analysis of network structures and dynamics than traditional methods.
  • This work bridges statistical mechanics, information theory, and network science for broader applications.