Inferring attitude in online social networks based on quadratic correlation
|Inferring attitude in online social networks based on quadratic correlation|
|Author(s)||Wang C., Bulatov A.A.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||machine learning, quadratic optimization, Signed Networks (Extra: 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))|
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Inferring attitude in online social networks based on quadratic correlation is a 2014 conference paper written in English by Wang C., Bulatov A.A. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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.
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