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Mendeley readers
Attention Score in Context
| Title |
Kavosh: a new algorithm for finding network motifs
|
|---|---|
| Published in |
BMC Bioinformatics, October 2009
|
| DOI | 10.1186/1471-2105-10-318 |
| Pubmed ID | |
| Authors |
Zahra Razaghi Moghadam Kashani, Hayedeh Ahrabian, Elahe Elahi, Abbas Nowzari-Dalini, Elnaz Saberi Ansari, Sahar Asadi, Shahin Mohammadi, Falk Schreiber, Ali Masoudi-Nejad |
| Abstract |
Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs. |
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 % |
|---|---|---|
| United States | 4 | 2% |
| Brazil | 3 | 2% |
| Netherlands | 2 | 1% |
| United Kingdom | 2 | 1% |
| Iran, Islamic Republic of | 2 | 1% |
| India | 1 | <1% |
| Portugal | 1 | <1% |
| Unknown | 148 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 46 | 28% |
| Researcher | 28 | 17% |
| Student > Master | 24 | 15% |
| Student > Postgraduate | 9 | 6% |
| Student > Bachelor | 9 | 6% |
| Other | 31 | 19% |
| Unknown | 16 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 62 | 38% |
| Agricultural and Biological Sciences | 28 | 17% |
| Biochemistry, Genetics and Molecular Biology | 15 | 9% |
| Mathematics | 11 | 7% |
| Engineering | 8 | 5% |
| Other | 20 | 12% |
| Unknown | 19 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 19 February 2021.
All research outputs
#5,500,307
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#1,994
of 7,234 outputs
Outputs of similar age
#25,578
of 93,646 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 59 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,234 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 71% 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 93,646 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 59 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 62% of its contemporaries.