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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

Summary

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.

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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:

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.

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