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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0241394 |
| _version_ | 1831760088968200192 |
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| author | Gerrit Großmann Luca Bortolussi Verena Wolf |
| author_facet | Gerrit Großmann Luca Bortolussi Verena Wolf |
| author_sort | Gerrit Großmann |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T03:43:36Z |
| format | Article |
| id | doaj.art-d94986a6334a4d96b60658b5077bc408 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T03:43:36Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d94986a6334a4d96b60658b5077bc4082022-12-21T18:40:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011510e024139410.1371/journal.pone.0241394Efficient simulation of non-Markovian dynamics on complex networks.Gerrit GroßmannLuca BortolussiVerena WolfWe 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.https://doi.org/10.1371/journal.pone.0241394 |
| spellingShingle | Gerrit Großmann Luca Bortolussi Verena Wolf Efficient simulation of non-Markovian dynamics on complex networks. PLoS ONE |
| title | Efficient simulation of non-Markovian dynamics on complex networks. |
| title_full | Efficient simulation of non-Markovian dynamics on complex networks. |
| title_fullStr | Efficient simulation of non-Markovian dynamics on complex networks. |
| title_full_unstemmed | Efficient simulation of non-Markovian dynamics on complex networks. |
| title_short | Efficient simulation of non-Markovian dynamics on complex networks. |
| title_sort | efficient simulation of non markovian dynamics on complex networks |
| url | https://doi.org/10.1371/journal.pone.0241394 |
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