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Emergent information dynamics in many-body interconnected systems
Wout Merbis1, Manlio de Domenico2
1Dutch Institute for Emergent Phenomena (DIEP), Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands.
Physical Review. E
|August 16, 2023
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.
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.
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