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

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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1729-1756
Number of pages28
JournalMachine Learning
Volume108
Issue number10
DOIs
Publication statusPublished - 14 Oct 2019

Keywords

  • Large networks
  • Learning to rank
  • Link prediction
  • Social network analysis
  • Supervised learning

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