RankMerging: A supervised learning-to-rank framework to predict links in large social network
This addresses link prediction in social networks, which is important for applications like recommendation systems, but the approach is incremental as it builds on existing unsupervised methods.
The paper tackles the problem of predicting missing links in large social networks by proposing RankMerging, a supervised learning-to-rank framework that combines unsupervised rankings, and shows it substantially improves performance over unsupervised metrics on three social networks.
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 metrics of ranking. We also compare it to other combination strategies based on standard methods. Finally, we explore various aspects of RankMerging, such as feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks.