Datasets of hypergraphs with power-law degree and order distributions used in "Efficient Gillespie algorithms for spreading phenomena in large and heterogeneous higher-order networks"

Datasets used as a supplement to the work "Efficient Gillespie algorithms for spreading phenomena in large and heterogeneous higher-order networks" by Hugo P. Maia, Wesley Cota, Yamir Moreno, and Silvio C. Ferreira. Reference: arxiv:2509.20174 This is part of the package hyperSIS, available at https://github.com/gisc-ufv/hyperSIS This dataset contains synthetic hypergraphs generated with a bipartite configuration model (BCM), adapted from Phys. Rev. E 93, 062311 (2016) There are $N$ nodes and $H$ hyperedges. Vertex degrees follow a power-law distribution $P_K \sim K^{-\gamma_k}$ with exponent $\gamma_k = 2.7$ Hyperedge orders also follow a power-law distribution $f_{m} \sim (m+1)^{-\gamma_m}$ with $\gamma_m = 2.5, 3.0$, and $6.0$. A rigid cutoff $K_c$ is imposed for the degree distribution:, defined by $N P_{K_c} = 1$. For $\gamma_m = 6.0$, a cutoff $m_c = 10$ is imposed, and for $\gamma_m=3.0$ and $2.5$ a rigid cutoff defined by $H f_{m_c} = 1$. Hypergraphs are simple: no self-loops and no repeated hyperedges. Vertices are indexed from $1$ to $N$. File format: Each file (.edgelist) lists the vertices belonging to one hyperedge per line. Files are organized by system size ($N$), hyperedge order exponent $\gamma_m$, and sample ID.