Pre-computed MCMC sample archives for "Degree-Preserving Randomization Methods for Network Analysis: Models, Algorithms, and Applications"

Pre-computed Markov chain Monte Carlo (MCMC) sample archives for the reproducibility of the ACM Computing Surveys paper "Degree-Preserving Randomization Methods for Network Analysis: Models, Algorithms, and Applications" (De Clerck, Van Utterbeeck, Rocha, 2026). The archive contains the JLD2 sample dumps that back the main-body figures (Karate club, Chesapeake Bay food web, Moreno crime bipartite network, escorts bipartite rating network), the MCMC convergence chain snapshots for four SNAP directed graphs (congress, slashdot, amazon, web), and the scaling-experiment samples for 63 synthetic graphs across 17 randomization methods. Extracting the tarball at the root of the code repository rehydrates every file to the location the experiment scripts expect. The samples let readers reproduce the paper's figures and tables in seconds instead of the 1–4 days of MCMC compute that regenerating from scratch requires. See README-samples.md inside the tarball for the exact contents, layout, and verification instructions. The MANIFEST.sha256 that ships with the tarball lets you check every extracted file against its recorded hash. Software counterpart (contains the experiment drivers, the RandomGraphs Julia package, and setup / download scripts): https://doi.org/10.5281/zenodo.21905550