  In many domains it is necessary to generate surrogate networks, e.g., for
hypothesis testing of different properties of a network. Furthermore,
generating surrogate networks typically requires that different properties of
the network is preserved, e.g., edges may not be added or deleted and the edge
weights may be restricted to certain intervals. In this paper we introduce a
novel efficient property-preserving Markov Chain Monte Carlo method termed
CycleSampler for generating surrogate networks in which (i) edge weights are
constrained to an interval and node weights are preserved exactly, and (ii)
edge and node weights are both constrained to intervals. These two types of
constraints cover a wide variety of practical use-cases. The method is
applicable to both undirected and directed graphs. We empirically demonstrate
the efficiency of the CycleSampler method on real-world datasets. We provide an
implementation of CycleSampler in R, with parts implemented in C.
