RankMerging: Learning-to-rank in large-scale social networks (extended version)

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. 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.

@article{tabourier2025_1407.2515,
  title={ RankMerging: A supervised learning-to-rank framework to predict links in large social network },
  author={ Lionel Tabourier and Daniel Faria Bernardes and Anne-Sophie Libert and Renaud Lambiotte },
  journal={arXiv preprint arXiv:1407.2515},
  year={ 2025 }
}