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Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

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Communicability in complex networks.

Ernesto Estrada¹, Naomichi Hatano

¹Complex Systems Research Group, X-Rays Unit, RIAIDT, Edificio CACTUS, University of Santiago de Compostela, Santiago de Compostela, Spain. estrada66@yahoo.com

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|June 4, 2008

Summary

We introduce a novel communicability measure for complex networks, generalizing shortest paths. Most networks show high communicability between popular nodes, enabling new classifications of complex systems.

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Area of Science:

Network Science
Complex Systems Analysis
Graph Theory

Background:

Complex networks are ubiquitous in nature and technology.
Understanding information flow and node interaction is crucial.
Existing measures like shortest paths have limitations in capturing global network properties.

Purpose of the Study:

To introduce a generalized communicability measure for complex networks.
To analyze structure-dynamic correlations and classify complex systems.
To identify and define network communities based on communicability.

Main Methods:

Development of a new network communicability measure, generalizing shortest paths.
Analysis of real-world networks to identify patterns of assortative and disassortative communicability.
Classification of complex systems into universality classes based on structure-dynamic correlations.
Definition and identification of network communities using the communicability measure.

Main Results:

Most real-world networks exhibit assortative communicability, with high interaction among highly connected nodes.
Some networks display disassortative communicability, where popular nodes interact poorly.
A classification scheme for complex systems based on structure-dynamic correlations was established.
The communicability measure effectively distinguishes network communities, defined by internal connectivity.

Conclusions:

The proposed communicability measure offers a powerful tool for analyzing complex networks.
Network communicability patterns can be used to classify diverse complex systems.
This measure provides a robust method for community detection in networks.