The Journal of Machine Learning Research
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
The power of convex relaxation: near-optimal matrix completion
IEEE Transactions on Information Theory
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
Who should share what?: item-level social influence prediction for users and posts ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Link prediction via matrix factorization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Exploiting longer cycles for link prediction in signed networks
Proceedings of the 20th ACM international conference on Information and knowledge management
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Friend or frenemy?: predicting signed ties in social networks
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Online social networks continue to witness a tremendous growth both in terms of the number of registered users and their mutual interactions. In this paper, we focus on online signed social networks where positive interactions among the users signify friendship or approval, whereas negative interactions indicate antagonism or disapproval. We introduce a novel problem which we call the link label prediction problem: Given the information about signs of certain links in a social network, we want to learn the nature of relationships that exist among the users by predicting the sign, positive or negative, of the remaining links. We propose a matrix factorization based technique MF-LiSP that exhibits strong generalization guarantees. We also investigate the applicability of logistic regression [8] in this setting. Our experiments on Wiki-Vote, Epinions and Slashdot data sets strongly corroborate the efficacy of these approaches.