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Twitter Demographics
Mendeley readers
Attention Score in Context
| Title |
Differentiable Graph Module (DGM) for Graph Convolutional Networks
|
|---|---|
| Published in |
IEEE Transactions on Software Engineering, February 2023
|
| DOI | 10.1109/tpami.2022.3170249 |
| Pubmed ID | |
| Authors |
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein |
Twitter Demographics
The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 8% |
| United Kingdom | 1 | 8% |
| France | 1 | 8% |
| Unknown | 9 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 75% |
| Scientists | 3 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 163 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 163 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 45 | 28% |
| Researcher | 21 | 13% |
| Student > Master | 20 | 12% |
| Student > Doctoral Student | 13 | 8% |
| Student > Bachelor | 10 | 6% |
| Other | 16 | 10% |
| Unknown | 38 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 78 | 48% |
| Engineering | 14 | 9% |
| Mathematics | 6 | 4% |
| Agricultural and Biological Sciences | 4 | 2% |
| Biochemistry, Genetics and Molecular Biology | 4 | 2% |
| Other | 17 | 10% |
| Unknown | 40 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 16 May 2022.
All research outputs
#1,753,651
of 23,567,572 outputs
Outputs from IEEE Transactions on Software Engineering
#128
of 6,033 outputs
Outputs of similar age
#33,995
of 428,044 outputs
Outputs of similar age from IEEE Transactions on Software Engineering
#5
of 173 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,033 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 97% 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 428,044 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 173 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.