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Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks
Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of—how to interpret the self-organized patterns to know more about the imminent crisi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4378512/ https://www.ncbi.nlm.nih.gov/pubmed/25822423 http://dx.doi.org/10.1038/srep09450 |
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author | Moon, Hankyu Lu, Tsai-Ching |
author_facet | Moon, Hankyu Lu, Tsai-Ching |
author_sort | Moon, Hankyu |
collection | PubMed |
description | Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of—how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description — of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible. |
format | Online Article Text |
id | pubmed-4378512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-43785122015-04-07 Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks Moon, Hankyu Lu, Tsai-Ching Sci Rep Article Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of—how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description — of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible. Nature Publishing Group 2015-03-30 /pmc/articles/PMC4378512/ /pubmed/25822423 http://dx.doi.org/10.1038/srep09450 Text en Copyright © 2015, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 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/4.0/ |
spellingShingle | Article Moon, Hankyu Lu, Tsai-Ching Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks |
title | Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks |
title_full | Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks |
title_fullStr | Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks |
title_full_unstemmed | Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks |
title_short | Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks |
title_sort | network catastrophe: self-organized patterns reveal both the instability and the structure of complex networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4378512/ https://www.ncbi.nlm.nih.gov/pubmed/25822423 http://dx.doi.org/10.1038/srep09450 |
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