# Randomisation Algorithms for Large Sparse Matrices

@article{Puolamki2018RandomisationAF, title={Randomisation Algorithms for Large Sparse Matrices}, author={Kai Puolam{\"a}ki and Andreas Henelius and Antti Ukkonen}, journal={Physical review. E}, year={2018}, volume={99 5-1}, pages={ 053311 } }

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…

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