A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Application of kernels to link analysis
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mopping up: modeling wikipedia promotion decisions
Proceedings of the 2008 ACM conference on Computer supported cooperative work
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
Controversial users demand local trust metrics: an experimental study on Epinions.com community
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Proceedings of the 18th ACM conference on Information and knowledge management
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Unsupervised transfer classification: application to text categorization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Statistical Analysis and Data Mining
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
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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.