Abstract
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.
Translated title of the contribution | RankMerging: A supervised learning-to-rank for predicting links in large-scale social networks |
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Original language | French |
Title of host publication | Extraction et Gestion des Connaissances, EGC 2015 |
Publisher | Cambridge University Press |
Pages | 395-400 |
Number of pages | 6 |
Volume | E.28 |
ISBN (Electronic) | 9782705690229 |
Publication status | Published - 1 Jan 2015 |
Event | 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 Duration: 27 Jan 2015 → 30 Jan 2015 |
Conference
Conference | 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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Country/Territory | Luxembourg |
City | Luxembourg |
Period | 27/01/15 → 30/01/15 |