FastGEMF: Scalable High-Speed Simulation of Stochastic Spreading Processes Over Complex Multilayer Networks

Predicting stochastic spreading processes across large-scale multi-layered networks remains a significant computational challenge due to the intricate interplay between network structure and spread dynamics. This study introduces FastGEMF, a novel, scalable simulation framework for exact, high-speed modeling of Markov chain processes on complex multi-layer networks. Inspired by the Gillespie algorithm and optimized for efficiency, FastGEMF achieves logarithmic time complexity per event, enabling simulations on networks with millions of nodes and edges without sacrificing accuracy. It introduces an event-driven algorithm with cautious update strategies, supporting diverse multi-compartment spreading processes. FastGEMF is implemented in Python programming language as an open-source package, providing accessibility to researchers and practitioners across domains such as epidemiology, cybersecurity, and information propagation, establishing an exact baseline for model validation and comparative analysis.