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Community Detection in Multilayer Networks Via Semi-Supervised Joint Symmetric Nonnegative Matrix Factorization

Overview of attention for article published in IEEE Transactions on Network Science and Engineering, December 2022
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About this Attention Score

  • Among the highest-scoring outputs from this source (#33 of 168)
  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

patent
1 patent

Readers on

mendeley
1 Mendeley
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Title
Community Detection in Multilayer Networks Via Semi-Supervised Joint Symmetric Nonnegative Matrix Factorization
Published in
IEEE Transactions on Network Science and Engineering, December 2022
DOI 10.1109/tnse.2022.3231593
Authors

Laishui Lv, Peng Hu, Dalal Bardou, Zijun Zheng, Ting Zhang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 100%
Readers by discipline Count As %
Social Sciences 1 100%
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 19 April 2022.
All research outputs
#8,544,090
of 25,392,582 outputs
Outputs from IEEE Transactions on Network Science and Engineering
#33
of 168 outputs
Outputs of similar age
#160,781
of 476,381 outputs
Outputs of similar age from IEEE Transactions on Network Science and Engineering
#1
of 6 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 168 research outputs from this source. They receive a mean Attention Score of 3.0. This one has gotten more attention than average, scoring higher than 57% 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 476,381 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 63% of its contemporaries.
We're also able to compare this research output to 6 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