Résumé
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.
Titre traduit de la contribution | RankMerging: A supervised learning-to-rank for predicting links in large-scale social networks |
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langue originale | Français |
titre | Extraction et Gestion des Connaissances, EGC 2015 |
Editeur | Cambridge University Press |
Pages | 395-400 |
Nombre de pages | 6 |
Volume | E.28 |
ISBN (Electronique) | 9782705690229 |
Etat de la publication | Publié - 1 janv. 2015 |
Evénement | Quinzieme Conference Internationale Francophone sur l'Extraction et la Gestion des Connaissances, EGC 2015 - 15th International French-Speaking Conference on Knowledge Extraction and Management, EGC 2015 - Luxembourg, Luxembourg Durée: 27 janv. 2015 → 30 janv. 2015 |
Une conférence
Une conférence | Quinzieme Conference Internationale Francophone sur l'Extraction et la Gestion des Connaissances, EGC 2015 - 15th International French-Speaking Conference on Knowledge Extraction and Management, EGC 2015 |
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Pays/Territoire | Luxembourg |
La ville | Luxembourg |
période | 27/01/15 → 30/01/15 |