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A latent space mapping for link prediction
Author(s)
Date Issued
2010-12-11
Date Available
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Subject – LCSH
Social sciences--Network analysis
Social networks--Mathematical models
Probabilities
Cluster analysis
Language
English
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
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