Our paper in Nature Communications:
Maia, H.P., Cota, W., Moreno, Y. et al. Efficient Gillespie algorithms for spreading phenomena in large and heterogeneous higher-order networks. Nat Commun 17, 8665 (2026). Efficient Gillespie algorithms for spreading phenomena in large and heterogeneous higher-order networks | Nature Communications
Code availability
The HB-OGA and NB-OGA codes are available at ( GitHub - gisc-ufv/hyperSIS: Optimized Gillespie Algorithms (OGA) for spreading processes on higher-order networks · GitHub ), ref. 56. The code is developed in Modern Fortran, it follows a modular, object-oriented structure and is compatible with the Fortran Package Manager (fpm), as well as a Python interface. A Jupyter Notebook with usage examples is provided. Network input can be supplied as a list of hyperedges, in bipartite format, in the XGI JSON format, or in the HIF standard format. Both temporal and quasi-stationary dynamics are available. The code was run using the LLVM-based Intel Fortran (ifx) and the non-commercial GNU Fortran (gfortran) compilers, on Linux and Windows Subsystem for Linux (WSL).
Also,
Cota, W., & Ferreira, S. C. (2017). Optimized Gillespie algorithms for the simulation of Markovian epidemic processes on large and heterogeneous networks. Computer Physics Communications, 219C, 303-312.