Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.
Sci Rep
; 5: 14286, 2015 Sep 21.
Article
em En
| MEDLINE
| ID: mdl-26387609
ABSTRACT
Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Segurança Computacional
/
Serviços de Informação
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Modelos Teóricos
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Croácia