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Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades
Two node variables determine the evolution of cascades in random networks: a node's degree and threshold. Correlations between both fundamentally change the robustness of a network, yet they are disregarded in standard analytic methods as local tree or heterogeneous mean field approximations, s...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Physical Society
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217536/ https://www.ncbi.nlm.nih.gov/pubmed/30253542 http://dx.doi.org/10.1103/PhysRevE.98.022306 |
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author | Burkholz, Rebekka Schweitzer, Frank |
author_facet | Burkholz, Rebekka Schweitzer, Frank |
author_sort | Burkholz, Rebekka |
collection | PubMed |
description | Two node variables determine the evolution of cascades in random networks: a node's degree and threshold. Correlations between both fundamentally change the robustness of a network, yet they are disregarded in standard analytic methods as local tree or heterogeneous mean field approximations, since order statistics are difficult to capture analytically because of their combinatorial nature. We show how they become tractable in the thermodynamic limit of infinite network size. This enables the analytic description of node attacks that are characterized by threshold allocations based on node degree. Using two examples, we discuss possible implications of irregular phase transitions and different speeds of cascade evolution for the control of cascades. |
format | Online Article Text |
id | pubmed-7217536 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | American Physical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-72175362020-05-13 Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades Burkholz, Rebekka Schweitzer, Frank Phys Rev E Articles Two node variables determine the evolution of cascades in random networks: a node's degree and threshold. Correlations between both fundamentally change the robustness of a network, yet they are disregarded in standard analytic methods as local tree or heterogeneous mean field approximations, since order statistics are difficult to capture analytically because of their combinatorial nature. We show how they become tractable in the thermodynamic limit of infinite network size. This enables the analytic description of node attacks that are characterized by threshold allocations based on node degree. Using two examples, we discuss possible implications of irregular phase transitions and different speeds of cascade evolution for the control of cascades. American Physical Society 2018-08-09 2018-08 /pmc/articles/PMC7217536/ /pubmed/30253542 http://dx.doi.org/10.1103/PhysRevE.98.022306 Text en ©2018 American Physical Society This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. |
spellingShingle | Articles Burkholz, Rebekka Schweitzer, Frank Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades |
title | Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades |
title_full | Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades |
title_fullStr | Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades |
title_full_unstemmed | Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades |
title_short | Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades |
title_sort | correlations between thresholds and degrees: an analytic approach to model attacks and failure cascades |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217536/ https://www.ncbi.nlm.nih.gov/pubmed/30253542 http://dx.doi.org/10.1103/PhysRevE.98.022306 |
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