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

TL;DR

FastGEMF is a new scalable framework that efficiently simulates stochastic spreading processes over large, complex multilayer networks, enabling exact predictions with reduced computational resources.

Contribution

It introduces a comprehensive, high-speed simulation framework for stochastic spreading processes across multi-layer networks with millions of nodes.

Findings

Enables exact simulation of spreading processes on large-scale networks

Reduces time and space complexity significantly

Provides a baseline for comparative analysis of spreading models

Abstract

Predicting the spread of processes across complex multi-layered networks has long challenged researchers due to the intricate interplay between network structure and propagation dynamics. Each layer of these networks possesses unique characteristics, further complicating analysis. To authors' knowledge, a comprehensive framework capable of simulating various spreading processes across different layers, particularly in networks with millions of nodes and connections, has been notably absent. This study introduces a novel framework that efficiently predicts Markov Chain processes over large-scale networks, while significantly reducing time and space complexity. This approach enables exact simulation of spreading processes across extensive real-world multi-layer networks, accounting for diverse influencers on each layer. FastGEMF provides a baseline framework for exact simulating stochastic spread processes, facilitating comparative analysis of models across diverse domains, from epidemiology to social media dynamics.