A latent space mapping for link prediction

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Title: A latent space mapping for link prediction
Authors: Brew, AnthonySalter-Townshend, Michael
Permanent link: http://hdl.handle.net/10197/2895
Date: 11-Dec-2010
Online since: 2011-04-08T12:16:08Z
Abstract: Network modeling can be approached using either discriminative or probabilistic models. In the task of link prediction a probabilistic model will give a probability for the existence of a link; while in some scenarios this may be beneficial, in others a hard discriminative boundary needs to be set. Hence the use of a discriminative classifier is preferable. In domains such as image analysis and speaker recognition, probabilistic models have been used as a mechanism from which features can be extracted. This paper examines using a probabilistic model built on the entire graph to extract features to predict the existence of unknown links between two nodes. It demonstrates how features extracted from the model as well as the predicted probability of a link existing can aid the classification process.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Keywords: NetworksLink predictionSocial network analysis
Subject LCSH: Social sciences--Network analysis
Social networks--Mathematical models
Cluster analysis
Other versions: http://morrislab.med.utoronto.ca/~anna/networksnips2010/Home_files/13_brew_latentmap-nips2010.pdf
Language: en
Status of Item: Peer reviewed
Conference Details: NIPS Workshop on Networks across Disciplines in Theory and Applications, 11th December 2010, Whistler BC, Canada
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-sa/1.0/
Appears in Collections:Computer Science Research Collection
Mathematics and Statistics Research Collection

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