RankMerging: a supervised learning-to-rank framework to predict links in large social networks

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

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.

langue originaleAnglais
journalMachine Learning
Les DOIs
étatPublié - 1 janv. 2019

Empreinte digitale

Supervised learning
Robustness (control systems)
Parameter estimation
Feature extraction
Computational complexity

mots-clés

    Citer ceci

    @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",
    year = "2019",
    month = "1",
    day = "1",
    doi = "10.1007/s10994-019-05792-4",
    language = "English",
    journal = "Machine Learning",
    issn = "0885-6125",
    publisher = "Springer New York",

    }

    RankMerging : a supervised learning-to-rank framework to predict links in large social networks. / Tabourier, Lionel; Bernardes, Daniel F.; Libert, Anne Sophie; Lambiotte, Renaud.

    Dans: Machine Learning, 01.01.2019.

    Résultats de recherche: Contribution à un journal/une revueArticle

    TY - JOUR

    T1 - RankMerging

    T2 - a supervised learning-to-rank framework to predict links in large social networks

    AU - Tabourier, Lionel

    AU - Bernardes, Daniel F.

    AU - Libert, Anne Sophie

    AU - Lambiotte, Renaud

    PY - 2019/1/1

    Y1 - 2019/1/1

    N2 - 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.

    AB - 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.

    KW - Large networks

    KW - Learning to rank

    KW - Link prediction

    KW - Social network analysis

    KW - Supervised learning

    UR - http://www.scopus.com/inward/record.url?scp=85063218991&partnerID=8YFLogxK

    U2 - 10.1007/s10994-019-05792-4

    DO - 10.1007/s10994-019-05792-4

    M3 - Article

    JO - Machine Learning

    JF - Machine Learning

    SN - 0885-6125

    ER -