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Self-Healing Networks: Redundancy and Structure

Overview of attention for article published in PLoS ONE, February 2014
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
31 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
74 Mendeley
citeulike
1 CiteULike
Title
Self-Healing Networks: Redundancy and Structure
Published in
PLoS ONE, February 2014
DOI 10.1371/journal.pone.0087986
Pubmed ID
Authors

Walter Quattrociocchi, Guido Caldarelli, Antonio Scala

Abstract

We introduce the concept of self-healing in the field of complex networks modelling; in particular, self-healing capabilities are implemented through distributed communication protocols that exploit redundant links to recover the connectivity of the system. We then analyze the effect of the level of redundancy on the resilience to multiple failures; in particular, we measure the fraction of nodes still served for increasing levels of network damages. Finally, we study the effects of redundancy under different connectivity patterns-from planar grids, to small-world, up to scale-free networks-on healing performances. Small-world topologies show that introducing some long-range connections in planar grids greatly enhances the resilience to multiple failures with performances comparable to the case of the most resilient (and least realistic) scale-free structures. Obvious applications of self-healing are in the important field of infrastructural networks like gas, power, water, oil distribution systems.

Twitter Demographics

The data shown below were collected from the profiles of 31 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 2 3%
Germany 1 1%
Unknown 71 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 3%
Student > Ph. D. Student 1 1%
Unknown 71 96%
Readers by discipline Count As %
Physics and Astronomy 3 4%
Unknown 71 96%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 30 August 2016.
All research outputs
#727,569
of 12,707,479 outputs
Outputs from PLoS ONE
#12,994
of 138,267 outputs
Outputs of similar age
#15,516
of 246,192 outputs
Outputs of similar age from PLoS ONE
#705
of 8,492 outputs
Altmetric has tracked 12,707,479 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 138,267 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. This one has done particularly well, scoring higher than 90% 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 246,192 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 8,492 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.