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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

Framework based on communicability and flow to analyze complex network dynamics.

M Gilson¹, N E Kouvaris^1,2, G Deco^1,3

¹Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain.

Physical Review. E

|June 17, 2018

Summary

This summary is machine-generated.

This study introduces a novel framework using network response over time to link complex network structure and dynamics. It defines a

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Area of Science:

Network science
Graph theory
Dynamical systems

Background:

Complex networks are analyzed using graph theory and dynamics (e.g., synchronization, random walks).
Discrepancies exist between network topology inference and dynamic system analysis.
A unified framework is needed to connect network structure and dynamics.

Purpose of the Study:

To propose a rigorous theoretical framework for studying the interplay between network structure and dynamics.
To define a measure ('flow') quantifying interactions between network connectivity and external inputs.
To establish a canonical relationship with directed and weighted networks.

Main Methods:

Utilizing the network response over time (Green function) to analyze node interactions.
Defining a multivariate 'flow' measure.
Illustrating the framework with the multivariate Ornstein-Uhlenbeck process.

Main Results:

The proposed framework rigorously connects network structure and dynamics.
The 'flow' measure relates to graph communicability and the map equation.
The theory is applicable to various local dynamics and network types.

Conclusions:

The developed framework offers a comprehensive approach to network analysis.
It enables a unified understanding from pairwise interactions to global network properties.
This work can revise standards for analyzing directed and weighted complex networks.