RankMerging: Learning to rank in large-scale social networks

Abstract : In this work, we consider the issue of unveiling unknown links in a social network, one of the difficulties of this problem being the small number of unobserved links in comparison of the total number of pairs of nodes. We define a simple supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. As an illustration, we apply the method to the case of a cell phone service provider, which uses the network among its contractors as a learning set to discover links existing among users of its competitors. We show that our method substantially improves the performance of unsupervised metrics of classification. Finally, we discuss how it can be used with additional sources of data, including temporal or semantic information.
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Lionel Tabourier, Anne-Sophie Libert, Renaud Lambiotte. RankMerging: Learning to rank in large-scale social networks. DyNakII, 2nd International Workshop on Dynamic Networks and Knowledge Discovery (PKDD 2014 workshop) , Sep 2014, Nancy, France. ⟨hal-01208510⟩

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