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- W2012678400 abstract "Under the different searches performed to analyzing social networks, much attention has been devoted to the problem of predicting links. It is a key technique in many applications such as recommendation systems which provide suggestions of potential links between nodes. Traditional link prediction methods use a single proximity metric. In this paper, we study link prediction as a supervised learning task where we try to combine multiple features as input data for classification. To improve the accuracy of prediction, we have been applying a select attributes algorithm. Experiments have been performed on two co-authorship data sets. Results demonstrate that Random Forest, k-nearest neighbors and Principal Component Analysis yield the best performances." @default.
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- W2012678400 date "2014-11-01" @default.
- W2012678400 modified "2023-10-15" @default.
- W2012678400 title "Feature based link prediction" @default.
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- W2012678400 doi "https://doi.org/10.1109/aiccsa.2014.7073243" @default.
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