Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Proceedings of the 18th international conference on World wide web
Inferring privacy information from social networks
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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With the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook1. Currently, the privacy issue of online social networks becomes a hot and dynamic research topic. Though some privacy protecting strategies are implemented, they are not stringent enough. Recently, Semi-Supervised Learning (SSL), which has the advantage of utilizing the unlabeled data to achieve better performance, attracts much attention from the web research community. By utilizing a large number of unlabeled data from websites, SSL can effectively infer hidden or sensitive information on the Internet. Furthermore, graph-based SSL is much more suitable for modeling real-world objects with graph characteristics, like online social networks. Thus, we propose a novel Community-based Graph (CG) SSL model that can be applied to exploit security issues in online social networks, then provide two consistent algorithms satisfying distinct needs. In order to evaluate the effectiveness of this model, we conduct a series of experiments on a synthetic data and two real-world data from StudiVZ2 and Facebook. Experimental results demonstrate that our approach can more accurately and confidently predict sensitive information of online users, comparing to previous models.