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Efficient generation of large random networks.

Vladimir Batagelj1, Ulrik Brandes

  • 1Department of Mathematics, University of Ljubljana, Slovenia. vladimir.batagelj@uni-lj.si

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 21, 2005
PubMed
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We present simple and efficient algorithms for generating large random networks quickly. These methods are linear in time and space, making them ideal for network statistics research.

Area of Science:

  • Network Science
  • Computer Science
  • Algorithmic Complexity

Background:

  • Random networks are crucial for studying model parameters and algorithm performance.
  • The growing interest in large-scale network statistics necessitates faster network generation methods.

Purpose of the Study:

  • To introduce simple and efficient algorithms for generating random networks.
  • To address the demand for rapid generation of numerous large networks.

Main Methods:

  • Development of algorithms with linear running time.
  • Development of algorithms with linear space requirements.
  • Implementation of algorithms for commonly used network models.

Main Results:

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  • Algorithms achieve linear time complexity relative to network size.
  • Algorithms achieve linear space complexity relative to network size.
  • Algorithms are demonstrated to be easily implementable.

    • Conclusions:

    • The presented algorithms efficiently generate large random networks.
    • These algorithms meet the demand for speed and scalability in network generation.
    • The simplicity and efficiency facilitate research in network statistics and algorithm testing.