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Botnet detection using graph-based feature clustering

Overview of attention for article published in Journal of Big Data, May 2017
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Mentioned by

twitter
1 X user

Citations

dimensions_citation
104 Dimensions

Readers on

mendeley
168 Mendeley
Title
Botnet detection using graph-based feature clustering
Published in
Journal of Big Data, May 2017
DOI 10.1186/s40537-017-0074-7
Authors

Sudipta Chowdhury, Mojtaba Khanzadeh, Ravi Akula, Fangyan Zhang, Song Zhang, Hugh Medal, Mohammad Marufuzzaman, Linkan Bian

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Czechia 1 <1%
Unknown 167 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 30 18%
Student > Ph. D. Student 27 16%
Student > Bachelor 13 8%
Researcher 11 7%
Lecturer 8 5%
Other 32 19%
Unknown 47 28%
Readers by discipline Count As %
Computer Science 75 45%
Engineering 19 11%
Business, Management and Accounting 4 2%
Agricultural and Biological Sciences 3 2%
Unspecified 3 2%
Other 9 5%
Unknown 55 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 May 2017.
All research outputs
#15,459,013
of 22,971,207 outputs
Outputs from Journal of Big Data
#201
of 339 outputs
Outputs of similar age
#194,884
of 310,140 outputs
Outputs of similar age from Journal of Big Data
#4
of 8 outputs
Altmetric has tracked 22,971,207 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 339 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 25th percentile – i.e., 25% 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 310,140 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.