Advances in Web Mining and Web Usage Analysis, 9 conf., by Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee

By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum

This ebook constitutes the completely refereed post-workshop court cases of the ninth foreign Workshop on Mining internet information, WEBKDD 2007, and the first overseas Workshop on Social community research, SNA-KDD 2007, together held in St. Jose, CA, united states in August 2007 along with the thirteenth ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2007.

The eight revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been rigorously chosen from 23 preliminary submisssions. the improved papers tackle all present matters in net mining and social community research, together with conventional internet and semantic internet functions, the rising functions of the internet as a social medium, in addition to social community modeling and analysis.

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Hubs-and-authorities” importance: “hub” refers to the vertex vi that points to many authorities, and “authority” is a vertex vj that points to many hubs. We used the recursive algorithm proposed by [18] that calculates the “hubsand-authorities” importance of each vertex of a graph G(V, E). 3 The Social Score We introduce the social score S, a normalized, scaled number between 0 and 100 which is computed for each user as a weighted combination of the number of emails, response score, average response time, clique scores, and the degree and centrality measures introduced above.

Mining newsgroups using networks arising from social behavior. In: Proceedings of the 12th International Conference on World Wide Web, pp. 529–535 (2003) 2. : Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002) 3. : Pajek - program for large network analysis. Connections 2(21), 47–57 (1998) 4. : The effect of adding relevance information in a relevance feedback environment. In: Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval.

7. 8. 9. number of emails average response time response score number of cliques raw clique score weighted clique score degree centrality clustering coefficient mean of shortest path length from a specific vertex to all vertices in the graph 10. betweenness centrality 11. “Hubs-and-Authorities” importance Finally, these weighted contributions are then normalized over the chosen weights wx to compute the social score as follows: S= wx · Cx all x wx all x This gives us a score between 0 and 100 with which to rank every user into an overall ranked list.

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