RankMerging: A supervised learning-to-rank framework to predict links in large social network
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2014
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916665199951872 |
|---|---|
| author | Tabourier, Lionel Bernardes, Daniel Faria Libert, Anne-Sophie Lambiotte, Renaud |
| author_facet | Tabourier, Lionel Bernardes, Daniel Faria Libert, Anne-Sophie Lambiotte, Renaud |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1407_2515 |
| institution | arXiv |
| publishDate | 2014 |
| record_format | arxiv |
| spellingShingle | RankMerging: A supervised learning-to-rank framework to predict links in large social network Tabourier, Lionel Bernardes, Daniel Faria Libert, Anne-Sophie Lambiotte, Renaud Social and Information Networks Information Retrieval Machine Learning Physics and Society 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. |
| title | RankMerging: A supervised learning-to-rank framework to predict links in large social network |
| topic | Social and Information Networks Information Retrieval Machine Learning Physics and Society |
| url | https://arxiv.org/abs/1407.2515 |