RankMerging: A supervised learning-to-rank for predicting links in large-scale social networks

Uncovering 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 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 aims at discovering links among contractors of its competitors. We show that our method substantially improves the performance of the available classification methods.