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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation

Overview of attention for article published in Journal of Cheminformatics, November 2019
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
Published in
Journal of Cheminformatics, November 2019
DOI 10.1186/s13321-019-0396-x
Authors

Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Won-Joon Son, Dongseon Lee, Seokho Kang

Twitter Demographics

The data shown below were collected from the profiles of 9 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Researcher 5 19%
Student > Master 4 15%
Student > Bachelor 4 15%
Lecturer > Senior Lecturer 1 4%
Other 4 15%
Unknown 2 8%
Readers by discipline Count As %
Computer Science 11 42%
Chemistry 8 31%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Physics and Astronomy 1 4%
Mathematics 1 4%
Other 1 4%
Unknown 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 24 November 2019.
All research outputs
#3,966,017
of 15,094,232 outputs
Outputs from Journal of Cheminformatics
#337
of 606 outputs
Outputs of similar age
#112,992
of 345,505 outputs
Outputs of similar age from Journal of Cheminformatics
#42
of 78 outputs
Altmetric has tracked 15,094,232 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 606 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one is in the 43rd percentile – i.e., 43% 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 345,505 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 67% of its contemporaries.
We're also able to compare this research output to 78 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.