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Hierarchical stochastic graphlet embedding for graph-based pattern recognition

Overview of attention for article published in Neural Computing and Applications, December 2019
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Mentioned by

twitter
4 X users
reddit
1 Redditor

Readers on

mendeley
32 Mendeley
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Article details
Title
Hierarchical stochastic graphlet embedding for graph-based pattern recognition
Published in
Neural Computing and Applications, December 2019
DOI 10.1007/s00521-019-04642-7
Authors

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Timeline Attention over time Attention Score history
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Activity
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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 7 22%
Student > Bachelor 4 13%
Researcher 4 13%
Student > Master 3 9%
Student > Postgraduate 3 9%
Other 4 13%
Unknown 7 22%
Readers by discipline
Readers by discipline Count As %
Computer Science 13 41%
Engineering 4 13%
Biochemistry, Genetics and Molecular Biology 1 3%
Mathematics 1 3%
Psychology 1 3%
Other 2 6%
Unknown 10 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 January 2020.
All research outputs
#20,525,530
of 32,041,085 outputs
Outputs from Neural Computing and Applications
#863
of 3,019 outputs
Outputs of similar age
#291,118
of 507,294 outputs
Outputs of similar age from Neural Computing and Applications
#22
of 33 outputs
Altmetric has tracked 32,041,085 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,019 research outputs from this source. They receive a mean Attention Score of 2.0. This one has gotten more attention than average, scoring higher than 70% 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 507,294 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.