Exploit of online social networks with community-based graph semi-supervised learning
Refereed conference paper presented and published in conference proceedings

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AbstractWith the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook. 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 StudiVZ and Facebook. Experimental results demonstrate that our approach can more accurately and confidently predict sensitive information of online users, comparing to previous models. © 2010 Springer-Verlag.
All Author(s) ListMo M., King I.
Name of Conference17th International Conference on Neural Information Processing, ICONIP 2010
Start Date of Conference22/11/2010
End Date of Conference25/11/2010
Place of ConferenceSydney, NSW
Country/Region of ConferenceAustralia
Detailed descriptionorganized by the Asia-Pacific Neural Network Assembly (APNNA),
Volume Number6443 LNCS
Issue NumberPART 1
PublisherSpringer Verlag
Place of PublicationGermany
Pages669 - 678
LanguagesEnglish-United Kingdom
Keywordscommunity consistency, graph-based semi-supervised learning, privacy issue, social network

Last updated on 2021-26-02 at 00:38