The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
IEEE Intelligent Systems
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On the collective classification of email "speech acts"
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
An Examination of Experimental Methodology for Classifiers of Relational Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Applying Link-Based Classification to Label Blogs
Advances in Web Mining and Web Usage Analysis
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we address the problem of classifying online social network users using a naively anonymized version of a social graph. We use two main user attributes defined by the graph structure to build an initial classifier, node degree and clustering coefficient, and then exploit user relationships to build a second classifier. We describe how to combine these two classifiers to build an Online Social Network (OSN) user classifier and then we evaluate the performance of our architecture by trying to solve two different classification problems (a binary and a multiclass problem) using data extracted from Twitter. Results show that the proposed classifier is sound and that both classification problems are feasible to solve by an attacker who is able to obtain a naively anonymized version of the social graph.