Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Jordan Canonical Form of the Google Matrix: A Potential Contribution to the PageRank Computation
SIAM Journal on Matrix Analysis and Applications
Interpreting social science link analysis research: A theoretical framework
Journal of the American Society for Information Science and Technology
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Choosing a leader on a complex network
Journal of Computational and Applied Mathematics
Different Aspects of Social Network Analysis
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Social networks, gender, and friending: An analysis of MySpace member profiles
Journal of the American Society for Information Science and Technology
MatLink: enhanced matrix visualization for analyzing social networks
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
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In this paper a new method to classify the users of an SNS (Social Network Site) into groups is shown. The method is based on the PageRank algorithm. Competitivity groups are sets of nodes that compete among each other to gain PageRank via the personalization vector. Specific features of the SNSs (such as number of friends or activity of the users) can modify the ranking inside each Competitivity group. The concept of Leadership Group is also presented. Some numerical examples are shown. These concepts can also be applied to general complex networks.