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Randomization algorithms for large sparse networks
2019
Physical review. E
In many domains it is necessary to generate surrogate networks, e.g., for hypothesis testing of different properties of a network. Generating surrogate networks typically requires that different properties of the network are preserved, e.g., edges may not be added or deleted and edge weights may be restricted to certain intervals. In this paper we present an efficient property-preserving Markov chain Monte Carlo method termed CycleSampler for generating surrogate networks in which (1) edge
doi:10.1103/physreve.99.053311
fatcat:4bmdjcwb4fgv3hyeqemi7kvfmm