On quantification and maximization of information transfer in network dynamical systems.
Moirangthem Sailash Singh1, Ramkrishna Pasumarthy2, Umesh Vaidya3
1Electrical Department, IIT Madras, Chennai, India. sailashm@gmail.com.
Scientific Reports
|April 5, 2023
Summary
This study introduces a framework to quantify and control information flow in complex networks by integrating information and control theories. Reconfiguring network topology optimizes information transfer, demonstrated in brain networks.
Area of Science:
- Complex network theory
- Information science
- Control theory
Background:
- Information flow in complex networks reveals cause-effect relationships and node contributions to network dynamics.
- Network topology significantly influences information flow patterns.
- Understanding and controlling information flow is crucial for various network applications.
Purpose of the Study:
- To develop a unified framework for quantifying and controlling information flow in complex networks.
- To elucidate the relationship between network topology and functional patterns.
- To demonstrate the optimization of information transfer through network re-configuration.
Main Methods:
- Integration of information science and control network theory.
- Development of a framework to quantify and control information flow.
- Analysis of network topology and its impact on information transfer.
Main Results:
- The proposed framework successfully quantifies and controls information flow.
- Network topology can be designed or reconfigured to optimize information transfer between specific nodes.
- Proof-of-concept application in brain networks demonstrated optimized excitation levels in neural circuits.
Conclusions:
- Network topology is a key factor in managing information flow.
- The developed framework offers a method to optimize information transfer in diverse complex networks.
- This approach has potential applications in biological, sensor, and social networks.
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