Efficient simulation of non-Markovian dynamics on complex networks
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...
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| Veröffentlicht in: | PloS one 2020-10, Vol.15 (10), p.e0241394-e0241394 |
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| creator | Großmann, Gerrit Bortolussi, Luca Wolf, Verena |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0241394 |
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| subjects | Algorithms Computer applications Computer Simulation Costs Dynamical systems Informatics Markov Chains Monte Carlo Method Monte Carlo simulation Multiagent systems Neighborhoods Network topologies Simulation Social networks Statistical analysis Statistical methods Stochastic models Stochastic Processes Stochasticity Topology |
| title | Efficient simulation of non-Markovian dynamics on complex networks |
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