Efficient and exact sampling of simple graphs with given arbitrary degree sequence.
Uniform sampling from graphical realizations of a given degree sequence is a fundamental component in simulation-based measurements of network observables, with applications ranging from epidemics, through social networks to Internet modeling. Existing graph sampling methods are either link-swap based (Markov-Chain Monte Carlo algorithms) or stub-matching based (the Configuration Model). Both types are ill-controlled, with typically unknown mixing times for link-swap methods and uncontrolled rejections for the Configuration Model. Here we propose an efficient, polynomial time algorithm that generates statistically independent graph samples with a given, arbitrary, degree sequence. The algorithm provides a weight associated with each sample, allowing the observable to be measured either uniformly over the graph ensemble, or, alternatively, with a desired distribution. Unlike other algorithms, this method always produces a sample, without back-tracking or rejections. Using a central limit theorem-based reasoning, we argue, that for large , and for degree sequences admitting many realizations, the sample weights are expected to have a lognormal distribution. As examples, we apply our algorithm to generate networks with degree sequences drawn from power-law distributions and from binomial distributions.
Del Genio
Charo I
CI
Department of Physics, University of Houston, Houston, Texas, United States of America.
Kim
Hyunju
H
Toroczkai
Zoltán
Z
Bassler
Kevin E
KE
D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D015233:Models, Statistical; D008962:Models, Theoretical; D012494:Sampling Studies
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