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Title
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Recommending research collaborations using link prediction and random forest classifiers
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Author
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Abstract
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| We introduce a method to predict or recommend high-potential future (i.e., not yet realized) collaborations. The proposed method is based on a combination of link prediction and machine learning techniques. First, a weighted co-authorship network is constructed. We calculate scores for each node pair according to different measures called predictors. The resulting scores can be interpreted as indicative of the likelihood of future linkage for the given node pair. To determine the relative merit of each predictor, we train a random forest classifier on older data. The same classifier can then generate predictions for newer data. The top predictions are treated as recommendations for future collaboration. We apply the technique to research collaborations between cities in Africa, the Middle East and South-Asia, focusing on the topics of malaria and tuberculosis. Results show that the method yields accurate recommendations. Moreover, the method can be used to determine the relative strengths of each predictor. |
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Language
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English
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Source (journal)
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Scientometrics: an international journal for all quantitative aspects of the science of science and science policy. - Amsterdam
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Publication
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Amsterdam
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2014
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ISSN
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0138-9130
[print]
1588-2861
[online]
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DOI
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10.1007/S11192-013-1228-9
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Volume/pages
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101
:2
(2014)
, p. 1461-1473
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ISI
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000343609900034
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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