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Predicting Human Preferences Using the Block Structure of Complex Social Networks

Overview of attention for article published in PLoS ONE, September 2012
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

twitter
60 tweeters
facebook
2 Facebook pages
googleplus
5 Google+ users

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
55 Mendeley
citeulike
6 CiteULike
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Title
Predicting Human Preferences Using the Block Structure of Complex Social Networks
Published in
PLoS ONE, September 2012
DOI 10.1371/journal.pone.0044620
Pubmed ID
Authors

Roger Guimerà, Alejandro Llorente, Esteban Moro, Marta Sales-Pardo

Abstract

With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a "new" computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups.

Twitter Demographics

The data shown below were collected from the profiles of 60 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 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 11%
Spain 2 4%
United Kingdom 2 4%
Switzerland 2 4%
Finland 1 2%
Ireland 1 2%
Luxembourg 1 2%
Brazil 1 2%
Unknown 39 71%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 38%
Researcher 13 24%
Student > Master 6 11%
Professor > Associate Professor 5 9%
Professor 3 5%
Other 7 13%
Readers by discipline Count As %
Computer Science 14 25%
Physics and Astronomy 12 22%
Unspecified 4 7%
Economics, Econometrics and Finance 3 5%
Social Sciences 3 5%
Other 19 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 48. 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 04 July 2017.
All research outputs
#293,004
of 12,134,845 outputs
Outputs from PLoS ONE
#5,991
of 133,451 outputs
Outputs of similar age
#2,279
of 119,188 outputs
Outputs of similar age from PLoS ONE
#119
of 3,951 outputs
Altmetric has tracked 12,134,845 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 133,451 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done particularly well, scoring higher than 95% 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 119,188 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 98% of its contemporaries.
We're also able to compare this research output to 3,951 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 96% of its contemporaries.