![]() There was no additional external funding received for this study.Ĭompeting interests: The authors have declared that no competing interests exist. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. įunding: This work is fully supported by the Marsden Fund Council from Government funding, managed by Royal Society Te Apārangi (MAU1719) to CRL. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data underlying the results presented in the study are available from. ![]() Received: JanuAccepted: OctoPublished: October 23, 2020Ĭopyright: © 2020 Means et al. We provide our implementation of the method on the GitHub repository for immediate use by the research community, and demonstrate its application to three real-world networks for null-space comparisons as well as the study of dynamics of neuronal networks.Ĭitation: Means SA, Bläsche C, Laing CR (2020) A permutation method for network assembly. Our method successfully builds networks with order O(10 7) edges on the scale of minutes with a laptop running Matlab. Given a sequence of in- and out- degrees, the method can also produce simple graphs for sequences that satisfy conditions of graphicality. The graph space is sampled by the method non-uniformly, yet the algorithm provides weightings for the sample space across all possible realisations allowing computation of statistical averages of network metrics as if they were sampled uniformly. It further allows prescribing the overall percentage of such multiple connections-permitting exploration of a weighted synthetic network space unlike any other method currently available for comparison of real-world networks with controlled multi-edge proportion null spaces. Our method permits inclusion or exclusion of ‘multi-edges’, allowing assembly of weighted or binary networks. It combines directed edge-swapping and constrained Monte-Carlo edge-mixing for improving approximations to the given out-degree sequence until it is exactly matched. This method utilises permutations of initial adjacency matrix assemblies that conform to the prescribed in-degree sequence, yet violate the given out-degree sequence. Wolfram Language & System Documentation Center.We present a method for assembling directed networks given a prescribed bi-degree (in- and out-degree) sequence. "PermutationMatrix." Wolfram Language & System Documentation Center. Wolfram Research (2022), PermutationMatrix, Wolfram Language function, (updated 2023). Ĭite this as: Wolfram Research (2022), PermutationMatrix, Wolfram Language function, (updated 2023).
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