Difference between revisions of "Supervised Random Walk"
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== Summary == | == Summary == | ||
| − | This paper addresses the problem of [[AddressesProblem::Link Prediction]] using the method of [[UsesMethod::Random walk with restart]]. | + | This paper addresses the problem of [[AddressesProblem::Link Prediction]] using the method of [[UsesMethod::Random walk with restart]]. ''Supervised Random Walk'' is interesting because it ranks the nodes based the network information and also using rich node and edge attributes that exist in the dataset. The method is supervised learning task where the goal is to learn the parameters of the function that assigns the strength of the edge (probability of taking that edge) such that a random walker is more likely to reach nodes to which new links will be created in future. |
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| + | This method is used to recommend friends on [[UsesDataset::Facebook dataset]] and also in predict links in collaboration network in [[UsesDataset::arXive]] database. | ||
== Method == | == Method == | ||
Revision as of 02:32, 30 March 2011
This is one of the paper discussed and written in course Social Media Analysis 10-802 in Spring 2011
Contents
Citation
Lars Backstrom & Jure Leskovec "Supervised Random Walks: Predicting and Recommending Links in Social Networks"
Online version
Summary
This paper addresses the problem of Link Prediction using the method of Random walk with restart. Supervised Random Walk is interesting because it ranks the nodes based the network information and also using rich node and edge attributes that exist in the dataset. The method is supervised learning task where the goal is to learn the parameters of the function that assigns the strength of the edge (probability of taking that edge) such that a random walker is more likely to reach nodes to which new links will be created in future.
This method is used to recommend friends on Facebook dataset and also in predict links in collaboration network in arXive database.
