1. Efficient simulation of non-Markovian dynamics on complex networks
- Author
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Gerrit Großmann, Verena Wolf, Luca Bortolussi, Grossmann, G., Bortolussi, L., and Wolf, V.
- Subjects
Informatics ,Computer science ,Science ,Markov models ,Markov process ,Probability density function ,Network topology ,Markov model ,Topology ,01 natural sciences ,010305 fluids & plasmas ,symbols.namesake ,Agent-based modeling ,Network analysis ,Simulation and modeling ,0103 physical sciences ,Computer Simulation ,010306 general physics ,Stochastic Processes ,Multidisciplinary ,Node (networking) ,Network analysi ,Complex network ,Markov Chains ,symbols ,Medicine ,State (computer science) ,Monte Carlo Method ,Algorithms ,Research Article - Abstract
We study continuous-time multi-agent models, where agents interact according to a network topology. At any point in time, each agent occupies a specific local node state. Agents change their state at random through interactions with neighboring agents. The time until a transition happens can follow an arbitrary probability density. Stochastic (Monte-Carlo) simulations are often the preferred-sometimes the only feasible-approach to study the complex emerging dynamical patterns of such systems. However, each simulation run comes with high computational costs mostly due to updating the instantaneous rates of interconnected agents after each transition. This work proposes a stochastic rejection-based, event-driven simulation algorithm that scales extremely well with the size and connectivity of the underlying contact network and produces statistically correct samples. We demonstrate the effectiveness of our method on different information spreading models. more...
- Published
- 2020
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