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Predicting Positive and Negative Links in Signed Social Networks by Transfer Learning
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BibTeX
@MISC{Ye_predictingpositive,
author = {Jihang Ye and Hong Cheng and Zhe Zhu and Minghua Chen},
title = {Predicting Positive and Negative Links in Signed Social Networks by Transfer Learning},
year = {}
}
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Abstract
Different from a large body of research on social networks that has focused almost exclusively on positive relationships, we study signed social networks with both positive and negative links. Specifically, we focus on how to reliably and effectively predict the signs of links in a newly formed signed social network (called a target network). Since usually only a very small amount of edge sign information is available in such newly formed networks, this small quantity is not adequate to train a good classifier. To address this challenge, we need assistance from an existing, mature signed network (called a source network) which has abundant edge sign information. We adopt the transfer learning approach to leverage the edge sign information from the source network, which may have a different yet related joint distribution of the edge instances and their class labels. As there is no predefined feature vector for the edge instances in a signed network, we construct generalizable features that can transfer the topological knowledge from the source network to the target. With the extracted features, we adopt an AdaBoost-like transfer learning algorithm with instance weighting to utilize more useful training instances in the source network for model learning. Experimental results on three real large signed social networks demonstrate that our transfer learning algorithm can improve the prediction accuracy by 40 % over baseline methods.
Keyphrases
social network source network negative link signed social network transfer learning edge sign information edge instance prediction accuracy good classifier small amount small quantity useful training instance target network positive relationship generalizable feature abundant edge sign information class label adaboost-like transfer joint distribution signed network model learning baseline method large body predefined feature vector extracted feature topological knowledge experimental result
