A unifying framework for fast randomization of ecological networks with fixed (node) degrees

• Published in: MethodsX Vol. 5; pp. 773 - 780
• Main Authors: Carstens, Corrie Jacobien, Berger, Annabell, Strona, Giovanni
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2018; Elsevier
• Subjects: Agricultural and Biological Science; algorithms; Binary matrices; Co-occurrence; community ecology; Curveball algorithm; Food web; mathematical models; Null model; Curveball algorithms to randomize networks with fixed node degrees; Curveball algorithm; Food web; Co-occurrence; Null model; Binary matrices
• ISSN: 2215-0161; 2215-0161
• DOI: 10.1016/j.mex.2018.06.018

[Display omitted] 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.