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Inferring attitude in online social networks based on quadratic correlation
Abstract The structure of an online social network The structure of an online social network in most cases cannot be described just by links between its members. We study online social networks, in which members may have certain attitude, positive or negative, toward each other, and so the network consists of a mixture of both positive and negative relationships. Our goal is to predict the sign of a given relationship based on the evidences provided in the current snapshot of the network. More precisely, using machine learning techniques we develop a model that after being trained on a particular network predicts the sign of an unknown or hidden link. The model uses relationships and influences from peers as evidences for the guess, however, the set of peers used is not predefined but rather learned during the training process. We use quadratic correlation between peer members to train the predictor. The model is tested on popular online datasets such as Epinions, Slashdot, and Wikipedia. In many cases it shows almost perfect prediction accuracy. Moreover, our model can also be efficiently updated as the underlying social network evolves. as the underlying social network evolves.
Abstractsub The structure of an online social network The structure of an online social network in most cases cannot be described just by links between its members. We study online social networks, in which members may have certain attitude, positive or negative, toward each other, and so the network consists of a mixture of both positive and negative relationships. Our goal is to predict the sign of a given relationship based on the evidences provided in the current snapshot of the network. More precisely, using machine learning techniques we develop a model that after being trained on a particular network predicts the sign of an unknown or hidden link. The model uses relationships and influences from peers as evidences for the guess, however, the set of peers used is not predefined but rather learned during the training process. We use quadratic correlation between peer members to train the predictor. The model is tested on popular online datasets such as Epinions, Slashdot, and Wikipedia. In many cases it shows almost perfect prediction accuracy. Moreover, our model can also be efficiently updated as the underlying social network evolves. as the underlying social network evolves.
Bibtextype inproceedings  +
Doi 10.1007/978-3-319-06608-0_12  +
Has author Chao Wang + , Bulatov A.A. +
Has extra keyword Artificial intelligence + , Data mining + , Learning systems + , Online systems + , Quadratic programming + , Machine learning techniques + , Model use + , On-line social networks + , Prediction accuracy + , Quadratic optimization + , Signed networks + , Training process + , Wikipedia + , Social networking (online) +
Has keyword Machine learning + , Quadratic optimization + , Signed Networks +
Issn 3029743  +
Issue PART 1  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 139–150  +
Published in Lecture Notes in Computer Science +
Title Inferring attitude in online social networks based on quadratic correlation +
Type conference paper  +
Volume 8443 LNAI  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 20:52:54  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 20:52:54  +
DateThis property is a special property in this wiki. 2014  +
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