Author(s): Batagelj V, Brandes U
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Abstract Random networks are frequently generated, for example, to investigate the effects of model parameters on network properties or to test the performance of algorithms. Recent interest in the statistics of large-scale networks sparked a growing demand for network generators that can generate large numbers of large networks quickly. We here present simple and efficient algorithms to randomly generate networks according to the most commonly used models. Their running time and space requirement is linear in the size of the network generated, and they are easily implemented.
This article was published in Phys Rev E Stat Nonlin Soft Matter Phys
and referenced in Journal of Health & Medical Informatics