Large order fluctuations, switching, and control in complex networks
Abstract We propose an analytical technique to study large fluctuations and switching from internal noise in complex networks. Using order-disorder kinetics as a generic example, we construct and analyze the most probable, or optimal path of fluctuations from one ordered state to another in real and...
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Nature Publishing Group
2017-09-01
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| Series: | Scientific Reports |
| Online Access: | http://link.springer.com/article/10.1038/s41598-017-08828-8 |
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doaj-art-1587dcf6443a43fd802a86510dc2a2062018-09-09T11:57:29ZengNature Publishing GroupScientific Reports2045-23222017-09-01711910.1038/s41598-017-08828-8Large order fluctuations, switching, and control in complex networksJason Hindes0Ira B. Schwartz1U.S. Naval Research Laboratory, Code 6792, Plasma Physics Division, Nonlinear Systems Dynamics SectionU.S. Naval Research Laboratory, Code 6792, Plasma Physics Division, Nonlinear Systems Dynamics SectionAbstract We propose an analytical technique to study large fluctuations and switching from internal noise in complex networks. Using order-disorder kinetics as a generic example, we construct and analyze the most probable, or optimal path of fluctuations from one ordered state to another in real and synthetic networks. The method allows us to compute the distribution of large fluctuations and the time scale associated with switching between ordered states for networks consistent with mean-field assumptions. In general, we quantify how network heterogeneity influences the scaling patterns and probabilities of fluctuations. For instance, we find that the probability of a large fluctuation near an order-disorder transition decreases exponentially with the participation ratio of a network’s principle eigenvector – measuring how many nodes effectively contribute to an ordered state. Finally, the proposed theory is used to answer how and where a network should be targeted in order to optimize the time needed to observe a switch.http://link.springer.com/article/10.1038/s41598-017-08828-8 |
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Open Data Bank |
| collection |
Open Access Journals |
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| language |
English |
| format |
Article |
| author |
Jason Hindes Ira B. Schwartz |
| spellingShingle |
Jason Hindes Ira B. Schwartz Large order fluctuations, switching, and control in complex networks Scientific Reports |
| author_facet |
Jason Hindes Ira B. Schwartz |
| author_sort |
Jason Hindes |
| title |
Large order fluctuations, switching, and control in complex networks |
| title_short |
Large order fluctuations, switching, and control in complex networks |
| title_full |
Large order fluctuations, switching, and control in complex networks |
| title_fullStr |
Large order fluctuations, switching, and control in complex networks |
| title_full_unstemmed |
Large order fluctuations, switching, and control in complex networks |
| title_sort |
large order fluctuations, switching, and control in complex networks |
| publisher |
Nature Publishing Group |
| series |
Scientific Reports |
| issn |
2045-2322 |
| publishDate |
2017-09-01 |
| description |
Abstract We propose an analytical technique to study large fluctuations and switching from internal noise in complex networks. Using order-disorder kinetics as a generic example, we construct and analyze the most probable, or optimal path of fluctuations from one ordered state to another in real and synthetic networks. The method allows us to compute the distribution of large fluctuations and the time scale associated with switching between ordered states for networks consistent with mean-field assumptions. In general, we quantify how network heterogeneity influences the scaling patterns and probabilities of fluctuations. For instance, we find that the probability of a large fluctuation near an order-disorder transition decreases exponentially with the participation ratio of a network’s principle eigenvector – measuring how many nodes effectively contribute to an ordered state. Finally, the proposed theory is used to answer how and where a network should be targeted in order to optimize the time needed to observe a switch. |
| url |
http://link.springer.com/article/10.1038/s41598-017-08828-8 |
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1612608592359194624 |