Carstens CJ, Berger A, Strona G. A unifying framework for fast randomization of ecological networks with fixed (node) degrees.
MethodsX 2018;
5:773-780. [PMID:
30094204 PMCID:
PMC6072652 DOI:
10.1016/j.mex.2018.06.018]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 06/25/2018] [Indexed: 11/20/2022] Open
Abstract
The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (or their matrix counterpart) while preserving node degrees. Here we introduce two extensions of the procedure, making it capable to randomize also unimode directed and undirected networks. We provide formal mathematical proofs that the two extensions, as the original Curveball, are fast and unbiased (i.e. they sample uniformly from the universe of possible network configurations).
We extend the Curveball algorithm to unimode directed and undirected networks.
As the original Curveball, extensions are fast and unbiased.
We provide Python and R code implementing the new procedures.
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