PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient generation of large random networks
Vladimir Batagelj and Ulrik Brandes
Phys. rev., E Stat. nonlinear soft matter phys. Volume 71, Number 3, pp. 1-5, 2005. ISSN 1539-3755

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 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 ecient 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
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:2849
Deposited By:Primož Lukšic
Deposited On:22 November 2006