RankMerging: a supervised learning-to-rank framework to predict links in large social networks View Full Text


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Article Info

DATE

2019-03-19

AUTHORS

Lionel Tabourier, Daniel F. Bernardes, Anne-Sophie Libert, Renaud Lambiotte

ABSTRACT

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised methods of ranking as well as standard supervised combination strategies. We also describe various properties of RankMerging, such as its computational complexity, its robustness to feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks. More... »

PAGES

1-28

References to SciGraph publications

  • 2011. Link Prediction via Matrix Factorization in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2015-12. Evaluating link prediction methods in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2009-10. Predicting missing links via local information in THE EUROPEAN PHYSICAL JOURNAL B
  • 2010-06. Efficient algorithms for ranking with SVMs in INFORMATION RETRIEVAL JOURNAL
  • 2013-06. Supervised methods for multi-relational link prediction in SOCIAL NETWORK ANALYSIS AND MINING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10994-019-05792-4

    DOI

    http://dx.doi.org/10.1007/s10994-019-05792-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112881377


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