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Related Experiment Video

Updated: Jun 19, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Information mobility in complex networks.

Ernesto Estrada¹

¹Department of Physics, Department of Mathematics, and Institute of Complex Systems, University of Strathclyde, Glasgow G1 1XH, United Kingdom. ernesto.estrada@strath.ac.uk

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

|October 2, 2009

Summary

This summary is machine-generated.

This study introduces information mobility in complex networks using fractional time stochastic processes. Scale-free networks show better information spread, but some real-world networks outperform them due to specific structural parameters.

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Area of Science:

Network Science
Statistical Physics
Information Theory

Background:

Complex networks are crucial for information dissemination.
Understanding information flow dynamics is essential for network analysis.
Existing models often lack a nuanced view of information mobility over time.

Purpose of the Study:

To introduce and define information mobility in complex networks.
To develop a framework using fractional calculus for analyzing information spread.
To compare information spreading efficiency in scale-free versus non-scale-free networks and real-world examples.

Main Methods:

Modeling information spread as a stochastic process on a network.
Utilizing fractional powers of the network's transition matrix.
Introducing a mobility coefficient based on the trace of fractional matrix powers.
Defining a metric for 50% information diffusion time (1/k(50)).

Main Results:

Scale-free random networks demonstrate superior information spreading capabilities compared to non-scale-free networks.
Analysis of 38 real-world networks reveals that some exhibit even better information diffusion than scale-free networks with similar average degrees.
Identification of key structural parameters contributing to enhanced information spread in real-world networks.

Conclusions:

Fractional calculus provides a powerful tool for quantifying information mobility in complex networks.
Network topology significantly impacts information diffusion efficiency.
Real-world network structures can possess unique properties that optimize information spread beyond idealized scale-free models.