Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Hubs, authorities, and communities
ACM Computing Surveys (CSUR)
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
Proceedings of the 10th international conference on Intelligent user interfaces
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Friends and foes: ideological social networking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Competitive collaborative learning
Journal of Computer and System Sciences
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with trust and distrust relationships
Proceedings of the third ACM conference on Recommender systems
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
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Maximizing acceptance probability for active friending in online social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
What is the added value of negative links in online social networks?
Proceedings of the 22nd international conference on World Wide Web
Link label prediction in signed social networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Exploiting local and global social context for recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
We study the problem of labeling the edges of a social network graph (e.g., acquaintance connections in Facebook) as either positive (i.e., trust, true friendship) or negative (i.e., distrust, possible frenemy) relations. Such signed relations provide much stronger signal in tying the behavior of online users than the unipolar Homophily effect, yet are largely unavailable as most social graphs only contain unsigned edges. We show the surprising fact that it is possible to infer signed social ties with good accuracy solely based on users' behavior of decision making (or using only a small fraction of supervision information) via unsupervised and semi-supervised algorithms. This work hereby makes it possible to turn an unsigned acquaintance network (e.g. Facebook, Myspace) into a signed trust-distrust network (e.g. Epinion, Slashdot). Our results are based on a mixed effects framework that simultaneously captures users' behavior, social interactions as well as the interplay between the two. The framework includes a series of latent factor models and it also encodes the principles of balance and status from Social psychology. Experiments on Epinion and Yahoo! Pulse networks illustrate that (1) signed social ties can be predicted with high-accuracy even in fully unsupervised settings, and (2) the predicted signed ties are significantly more useful for social behavior prediction than simple Homophily.