Randomizing Hypergraphs Preserving Degree Correlation and Local Clustering

Many complex systems involve direct interactions among more than two entities and can be represented by hypergraphs, in which hyperedges encode higher-order an arbitrary number of nodes. To analyze structures dynamics given a solid practice is to compare them with those for randomized hypergraphs that preserve some specific properties the original hypergraphs. In present study, we propose family such reference models called hyper dK-series, extending so-called dK-series dyadic networks case The preserves up individual node's degree, degree correlation, redundancy coefficient, and/or hyperedge's size depending on parameter values. We also apply numerical simulations epidemic spreading evolutionary game empirical