Can you infect me now?: malware propagation in mobile phone networks
Proceedings of the 2007 ACM workshop on Recurring malcode
Privacy homomorphisms for social networks with private relationships
Computer Networks: The International Journal of Computer and Telecommunications Networking
Social Network Analysis of 45,000 Schools: A Case Study of Technology Enhanced Learning in Europe
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Using social networks to distort users' profiles generated by web search engines
Computer Networks: The International Journal of Computer and Telecommunications Networking
Pass it on: social networks stymie censors
IPTPS'08 Proceedings of the 7th international conference on Peer-to-peer systems
A supervised machine learning link prediction approach for academic collaboration recommendation
Proceedings of the fourth ACM conference on Recommender systems
Exploiting social networks to provide privacy in personalized web search
Journal of Systems and Software
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OCSC'11 Proceedings of the 4th international conference on Online communities and social computing
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Social networks consist of a set of individuals and some form of social relationship that ties the individuals together. In this thesis, we use algorithmic techniques to study three aspects of social networks: (1) we analyze the "small-world" phenomenon by examining the geographic patterns of friendships in a large-scale social network, showing how this linkage pattern can itself explain the small-world results; (2) using existing patterns of friendship in a social network and a variety of graph-theoretic techniques, we show how to predict new relationships that will form in the network in the near future; and (3) we show how to infer social connections over which information flows in a network, by examining the times at which individuals in the network exhibit certain pieces of information, or interest in certain topics. Our approach is simultaneously theoretical and data-driven, and our results are based upon real experiments on real social-network data in addition to theoretical investigations of mathematical models of social networks. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)