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Attention Score in Context
| Chapter title |
Approximating Linear Threshold Predicates
|
|---|---|
| Chapter number | 9 |
| Book title |
Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
|
| Published in |
Lecture notes in computer science, February 2016
|
| DOI | 10.1007/978-3-642-15369-3_9 |
| Book ISBNs |
978-3-64-215368-6, 978-3-64-215369-3
|
| Authors |
Mahdi Cheraghchi, Johan Håstad, Marcus Isaksson, Ola Svensson |
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 13 March 2014.
All research outputs
#5,426,703
of 22,747,498 outputs
Outputs from Lecture notes in computer science
#1,697
of 8,126 outputs
Outputs of similar age
#88,464
of 396,715 outputs
Outputs of similar age from Lecture notes in computer science
#183
of 510 outputs
Altmetric has tracked 22,747,498 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 79% 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 396,715 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 510 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 64% of its contemporaries.