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Toward link predictability of complex networks

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, February 2015
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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 (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
35 tweeters
weibo
1 weibo user

Citations

dimensions_citation
230 Dimensions

Readers on

mendeley
282 Mendeley
citeulike
1 CiteULike
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Title
Toward link predictability of complex networks
Published in
Proceedings of the National Academy of Sciences of the United States of America, February 2015
DOI 10.1073/pnas.1424644112
Pubmed ID
Authors

Linyuan Lü, Liming Pan, Tao Zhou, Yi-Cheng Zhang, H. Eugene Stanley

Abstract

The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners.

Twitter Demographics

The data shown below were collected from the profiles of 35 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 282 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Portugal 1 <1%
Singapore 1 <1%
South Africa 1 <1%
Unknown 277 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 88 31%
Student > Master 40 14%
Researcher 25 9%
Professor > Associate Professor 16 6%
Student > Postgraduate 13 5%
Other 52 18%
Unknown 48 17%
Readers by discipline Count As %
Computer Science 90 32%
Physics and Astronomy 31 11%
Agricultural and Biological Sciences 21 7%
Mathematics 17 6%
Engineering 12 4%
Other 46 16%
Unknown 65 23%

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 09 September 2020.
All research outputs
#1,340,146
of 19,914,721 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#19,113
of 93,481 outputs
Outputs of similar age
#21,987
of 311,895 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#367
of 943 outputs
Altmetric has tracked 19,914,721 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 93,481 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.7. This one has done well, scoring higher than 79% 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 311,895 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 92% of its contemporaries.
We're also able to compare this research output to 943 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.