↓ Skip to main content

Robustness Elasticity in Complex Networks

Overview of attention for article published in PLOS ONE, July 2012
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

news
1 news outlet
twitter
2 X users

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
59 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 July 2012.
All research outputs
#3,514,302
of 25,193,883 outputs
Outputs from PLOS ONE
#46,067
of 218,525 outputs
Outputs of similar age
#22,248
of 169,940 outputs
Outputs of similar age from PLOS ONE
#702
of 3,958 outputs
Altmetric has tracked 25,193,883 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 218,525 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 169,940 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 3,958 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.