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RankMerging: A supervised learning-to-rank framework to predict links in large social network [article]

Lionel Tabourier, Daniel Faria Bernardes, Anne-Sophie Libert, Renaud Lambiotte
2019 arXiv   pre-print
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.  ...  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  ...  Acknowledgements The authors would like to thank Emmanuel Viennet and Maximilien Danisch for useful bibliographic indications.  ... 
arXiv:1407.2515v4 fatcat:z4argh5cjrfk3czzsponcjh2aa

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

Lionel Tabourier, Daniel F. Bernardes, Anne-Sophie Libert, Renaud Lambiotte
2019 Machine Learning  
In this paper, we define a simple yet efficient supervised learningto-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings.  ...  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  ...  Acknowledgements The authors would like to thank Emmanuel Viennet and Maximilien Danisch for useful bibliographic indications.  ... 
doi:10.1007/s10994-019-05792-4 fatcat:bzebxc2kfngc3aw27dplzfbngq

Predicting links in ego-networks using temporal information [article]

Lionel Tabourier, Anne-Sophie Libert, Renaud Lambiotte
2015 arXiv   pre-print
In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships.  ...  Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network.  ...  For this purpose, we use the learning-to-rank framework in [18] , especially designed for link prediction in large networks.  ... 
arXiv:1512.04776v1 fatcat:f6lynqiunffo7n5rsorzwzrsze

Predicting links in ego-networks using temporal information

Lionel Tabourier, Anne-Sophie Libert, Renaud Lambiotte
2016 EPJ Data Science  
In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships.  ...  Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network.  ...  For this purpose, we use the learning-to-rank framework in [], especially designed for link prediction in large networks.  ... 
doi:10.1140/epjds/s13688-015-0062-0 fatcat:xgxcbojcpvglrdfd4twhsdpjh4

A Holistic Approach for Predicting Links in Coevolving Multilayer Networks [article]

Alireza Hajibagheri, Gita Sukthankar, Kiran Lakkaraju
2016 arXiv   pre-print
This paper introduces a comprehensive framework, MLP (Multilayer Link Prediction), in which link existence likelihoods for the target layer are learned from the other network layers.  ...  These likelihoods are used to reweight the output of a single layer link prediction method that uses rank aggregation to combine a set of topological metrics.  ...  Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S.  ... 
arXiv:1609.03946v1 fatcat:iamtmjh55netzjom6c75avdcki