PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Generation of Large Random Networks
Vladimir Batagelj and Ulrik Brandes
Physical Review E Volume 71, Number 3, 036113, pp. 1-5, 2005. ISSN 1539-3755


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 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.

EPrint Type:Article
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Subjects:Theory & Algorithms
ID Code:1970
Deposited By:Vladimir Batagelj
Deposited On:07 January 2006