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

@article{Tabourier2019RankMergingAS,
  title={RankMerging: a supervised learning-to-rank framework to predict links in large social networks},
  author={Lionel Tabourier and Daniel Faria Bernardes and Anne-Sophie Libert and Renaud Lambiotte},
  journal={Machine Learning},
  year={2019},
  pages={1-28}
}
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… 
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