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Linear-size formulations for connected planar graph partitioning and political districting

Overview of attention for article published in Optimization Letters, October 2023
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About this Attention Score

  • Among the highest-scoring outputs from this source (#34 of 516)
  • Above-average Attention Score compared to outputs of the same age (61st percentile)

Mentioned by

twitter
3 X users

Readers on

mendeley
3 Mendeley
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Title
Linear-size formulations for connected planar graph partitioning and political districting
Published in
Optimization Letters, October 2023
DOI 10.1007/s11590-023-02070-0
Authors

Jack Zhang, Hamidreza Validi, Austin Buchanan, Illya V. Hicks

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 67%
Student > Ph. D. Student 1 33%
Readers by discipline Count As %
Engineering 2 67%
Unknown 1 33%
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 15 October 2023.
All research outputs
#14,303,111
of 24,619,469 outputs
Outputs from Optimization Letters
#34
of 516 outputs
Outputs of similar age
#76,168
of 203,115 outputs
Outputs of similar age from Optimization Letters
#1
of 1 outputs
Altmetric has tracked 24,619,469 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 516 research outputs from this source. They receive a mean Attention Score of 0.8. This one has done particularly well, scoring higher than 93% 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 203,115 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 61% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them