Controlling extreme events on complex networks
Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a pr...
| Main Authors: | , , |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
Nature Publishing Group
2014
|
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4135339/ |
| id |
pubmed-4135339 |
|---|---|
| recordtype |
oai_dc |
| spelling |
pubmed-41353392014-08-20 Controlling extreme events on complex networks Chen, Yu-Zhong Huang, Zi-Gang Lai, Ying-Cheng Article Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed. Nature Publishing Group 2014-08-18 /pmc/articles/PMC4135339/ /pubmed/25131344 http://dx.doi.org/10.1038/srep06121 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Chen, Yu-Zhong Huang, Zi-Gang Lai, Ying-Cheng |
| spellingShingle |
Chen, Yu-Zhong Huang, Zi-Gang Lai, Ying-Cheng Controlling extreme events on complex networks |
| author_facet |
Chen, Yu-Zhong Huang, Zi-Gang Lai, Ying-Cheng |
| author_sort |
Chen, Yu-Zhong |
| title |
Controlling extreme events on complex networks |
| title_short |
Controlling extreme events on complex networks |
| title_full |
Controlling extreme events on complex networks |
| title_fullStr |
Controlling extreme events on complex networks |
| title_full_unstemmed |
Controlling extreme events on complex networks |
| title_sort |
controlling extreme events on complex networks |
| description |
Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed. |
| publisher |
Nature Publishing Group |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4135339/ |
| _version_ |
1613125085903716352 |