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Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

Overview of attention for article published in Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

twitter
36 tweeters

Citations

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11 Dimensions

Readers on

mendeley
59 Mendeley
Title
Hierarchical mutual information for the comparison of hierarchical community structures in complex networks
Published in
Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, December 2015
DOI 10.1103/physreve.92.062825
Pubmed ID
Authors

Juan Ignacio Perotti, Claudio Juan Tessone, Guido Caldarelli

Abstract

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Twitter Demographics

The data shown below were collected from the profiles of 36 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
United Kingdom 3 5%
Switzerland 2 3%
Brazil 1 2%
Japan 1 2%
Netherlands 1 2%
Unknown 48 81%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 36%
Researcher 13 22%
Professor > Associate Professor 6 10%
Professor 5 8%
Student > Master 4 7%
Other 10 17%
Readers by discipline Count As %
Computer Science 13 22%
Unspecified 11 19%
Physics and Astronomy 10 17%
Agricultural and Biological Sciences 7 12%
Engineering 5 8%
Other 13 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 14 January 2016.
All research outputs
#798,859
of 12,826,420 outputs
Outputs from Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
#91
of 7,279 outputs
Outputs of similar age
#20,385
of 237,841 outputs
Outputs of similar age from Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
#4
of 215 outputs
Altmetric has tracked 12,826,420 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,279 research outputs from this source. They receive a mean Attention Score of 2.0. This one has done particularly well, scoring higher than 98% 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 237,841 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 215 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.