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Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario

Overview of attention for article published in IEEE/ACM Transactions on Networking, July 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario
Published in
IEEE/ACM Transactions on Networking, July 2022
DOI 10.1109/tnet.2022.3187310
Authors

Federico Mason, Gianfranco Nencioni, Andrea Zanella

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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 15%
Student > Bachelor 2 8%
Student > Doctoral Student 2 8%
Researcher 2 8%
Lecturer 1 4%
Other 4 15%
Unknown 11 42%
Readers by discipline Count As %
Computer Science 9 35%
Engineering 4 15%
Business, Management and Accounting 1 4%
Unspecified 1 4%
Unknown 11 42%
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 05 July 2022.
All research outputs
#14,485,392
of 25,392,582 outputs
Outputs from IEEE/ACM Transactions on Networking
#1,032
of 1,156 outputs
Outputs of similar age
#178,362
of 439,890 outputs
Outputs of similar age from IEEE/ACM Transactions on Networking
#1
of 1 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,156 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 10th percentile – i.e., 10% of its peers scored the same or lower than it.
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 439,890 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 59% 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