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Cluster and propensity based approximation of a network

Overview of attention for article published in BMC Systems Biology, March 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
1 tweeter
wikipedia
1 Wikipedia page

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
58 Mendeley
citeulike
4 CiteULike
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Title
Cluster and propensity based approximation of a network
Published in
BMC Systems Biology, March 2013
DOI 10.1186/1752-0509-7-21
Pubmed ID
Authors

John Michael Ranola, Peter Langfelder, Kenneth Lange, Steve Horvath

Abstract

The models in this article generalize current models for both correlation networks and multigraph networks. Correlation networks are widely applied in genomics research. In contrast to general networks, it is straightforward to test the statistical significance of an edge in a correlation network. It is also easy to decompose the underlying correlation matrix and generate informative network statistics such as the module eigenvector. However, correlation networks only capture the connections between numeric variables. An open question is whether one can find suitable decompositions of the similarity measures employed in constructing general networks. Multigraph networks are attractive because they support likelihood based inference. Unfortunately, it is unclear how to adjust current statistical methods to detect the clusters inherent in many data sets.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Sweden 2 3%
Norway 1 2%
Netherlands 1 2%
Denmark 1 2%
Portugal 1 2%
Unknown 49 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 29%
Student > Ph. D. Student 15 26%
Student > Master 6 10%
Professor > Associate Professor 5 9%
Professor 4 7%
Other 9 16%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 52%
Computer Science 6 10%
Biochemistry, Genetics and Molecular Biology 4 7%
Medicine and Dentistry 2 3%
Physics and Astronomy 2 3%
Other 8 14%
Unknown 6 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 03 June 2018.
All research outputs
#3,591,102
of 13,028,155 outputs
Outputs from BMC Systems Biology
#243
of 1,076 outputs
Outputs of similar age
#38,679
of 144,376 outputs
Outputs of similar age from BMC Systems Biology
#5
of 20 outputs
Altmetric has tracked 13,028,155 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,076 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 76% 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 144,376 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.