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Robustness Elasticity in Complex Networks

Overview of attention for article published in PLOS ONE, July 2012
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Title
Robustness Elasticity in Complex Networks
Published in
PLOS ONE, July 2012
DOI 10.1371/journal.pone.0039788
Pubmed ID
Authors

Timothy C. Matisziw, Tony H. Grubesic, Junyu Guo

Abstract

Network robustness refers to a network's resilience to stress or damage. Given that most networks are inherently dynamic, with changing topology, loads, and operational states, their robustness is also likely subject to change. However, in most analyses of network structure, it is assumed that interaction among nodes has no effect on robustness. To investigate the hypothesis that network robustness is not sensitive or elastic to the level of interaction (or flow) among network nodes, this paper explores the impacts of network disruption, namely arc deletion, over a temporal sequence of observed nodal interactions for a large Internet backbone system. In particular, a mathematical programming approach is used to identify exact bounds on robustness to arc deletion for each epoch of nodal interaction. Elasticity of the identified bounds relative to the magnitude of arc deletion is assessed. Results indicate that system robustness can be highly elastic to spatial and temporal variations in nodal interactions within complex systems. Further, the presence of this elasticity provides evidence that a failure to account for nodal interaction can confound characterizations of complex networked systems.

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Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 2%
Finland 1 2%
United Kingdom 1 2%
Iran, Islamic Republic of 1 2%
Denmark 1 2%
Korea, Republic of 1 2%
United States 1 2%
Poland 1 2%
Serbia 1 2%
Other 0 0%
Unknown 50 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 27%
Researcher 8 14%
Professor > Associate Professor 7 12%
Student > Doctoral Student 6 10%
Other 6 10%
Other 12 20%
Unknown 4 7%
Readers by discipline Count As %
Computer Science 8 14%
Engineering 8 14%
Agricultural and Biological Sciences 6 10%
Social Sciences 6 10%
Biochemistry, Genetics and Molecular Biology 4 7%
Other 19 32%
Unknown 8 14%