↓ Skip to main content

A top-down supervised learning approach to hierarchical multi-label classification in networks

Overview of attention for article published in Applied Network Science, February 2022
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
5 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
25 Mendeley
Title
A top-down supervised learning approach to hierarchical multi-label classification in networks
Published in
Applied Network Science, February 2022
DOI 10.1007/s41109-022-00445-3
Authors

Miguel Romero, Jorge Finke, Camilo Rocha

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 16%
Researcher 3 12%
Student > Ph. D. Student 2 8%
Unspecified 1 4%
Professor 1 4%
Other 1 4%
Unknown 13 52%
Readers by discipline Count As %
Computer Science 4 16%
Engineering 3 12%
Arts and Humanities 1 4%
Agricultural and Biological Sciences 1 4%
Unspecified 1 4%
Other 2 8%
Unknown 13 52%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 March 2022.
All research outputs
#14,262,011
of 24,093,053 outputs
Outputs from Applied Network Science
#278
of 532 outputs
Outputs of similar age
#224,848
of 510,158 outputs
Outputs of similar age from Applied Network Science
#6
of 18 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 532 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
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 510,158 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 55% of its contemporaries.
We're also able to compare this research output to 18 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 72% of its contemporaries.