Fast Sequential Creation of Random Realizations of Degree Sequences

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Abstract

We examine the problem of creating random realizations of very large degree sequences. While fast in practice, the Markov chain Monte Carlo (MCMC) method for selecting a realization has limited usefulness for creating large graphs because of memory constraints. Instead, we focus on sequential importance sampling (SIS) schemes for random graph creation. A difficulty with SIS schemes is assuring that they terminate in a reasonable amount of time. We introduce a new sampling method where we guarantee termination while achieving speed comparable to the MCMC method.

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APA

Cloteaux, B. (2016). Fast Sequential Creation of Random Realizations of Degree Sequences. Internet Mathematics, 12(3), 205–219. https://doi.org/10.1080/15427951.2016.1164768

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