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Related Concept Videos

Sequence Networks of Rotating Machines01:24

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Modeling temporal networks using random itineraries.

Alain Barrat1, Bastien Fernandez2, Kevin K Lin3

  • 1Centre de Physique Théorique, Aix-Marseille Université, CNRS UMR 7332, Université de Toulon, 13288 Marseille cedex 9, France and Data Science Lab, Institute for Scientific Interchange (ISI) Foundation, Torino 10126, Italy.

Physical Review Letters
|August 29, 2014
PubMed
Summary
This summary is machine-generated.

We developed a method to create dynamic networks exhibiting bursty and repetitive behaviors using random walks. This approach accurately models real-world systems like transportation networks.

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Area of Science:

  • Complex systems
  • Network science
  • Computational modeling

Background:

  • Dynamical networks exhibit complex temporal behaviors.
  • Understanding these behaviors is crucial for modeling real-world systems.
  • Existing methods may not fully capture bursty or repetitive patterns.

Purpose of the Study:

  • To introduce a novel procedure for generating dynamical networks.
  • To create networks with adjustable features, including bursty and correlated temporal behaviors.
  • To demonstrate the procedure's applicability using a transportation system case study.

Main Methods:

  • Constructing networks by accumulating paths generated via random walks of variable length.
  • Defining a general framework for network generation.
  • Applying the procedure to a transportation network dataset.

Main Results:

  • The proposed procedure successfully generates dynamical networks with desired temporal properties.
  • The synthetic network generated from the transportation system case study accurately mimics empirical data.
  • Adjustable features allow for tailored network generation.

Conclusions:

  • The random walk-based procedure offers a flexible and effective method for generating realistic dynamical networks.
  • This approach can be valuable for simulating and analyzing complex systems.
  • The method provides a robust tool for creating synthetic network data with specific temporal characteristics.