@article{77cd5761620145f0b1f2713df42d8b0f,
title = "RankMerging: a supervised learning-to-rank framework to predict links in large social networks",
abstract = "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 methods of ranking as well as standard supervised combination strategies. We also describe various properties of RankMerging, such as its computational complexity, its robustness to feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks.",
keywords = "Large networks, Learning to rank, Link prediction, Social network analysis, Supervised learning",
author = "Lionel Tabourier and Bernardes, {Daniel F.} and Libert, {Anne Sophie} and Renaud Lambiotte",
note = "Funding Information: The authors would like to thank Emmanuel Viennet and Maximilien Danisch for useful bibliographic indications. This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office. The scientific responsibility rests with its authors. This work is also funded in part by the ANR (French National Agency of Research) under Grants ANR-15-CE38-0001 (AlgoDiv) and ANR-13-CORD-0017-01 (CODDDE), by the French program “PIA—Usages, services et contenus innovants” under Grant O18062-44430 (REQUEST), and by the Ile-de-France FUI21 program under Grant 16010629 (iTRAC). We also acknowledge support from FNRS. Publisher Copyright: {\textcopyright} 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.",
year = "2019",
month = oct,
day = "14",
doi = "10.1007/s10994-019-05792-4",
language = "English",
volume = "108",
pages = "1729--1756",
journal = "Machine Learning",
issn = "0885-6125",
publisher = "Springer New York",
number = "10",
}