By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen
This publication constitutes the completely refereed post-workshop complaints of the ninth foreign Workshop on Mining internet information, WEBKDD 2007, and the first foreign Workshop on Social community research, SNA-KDD 2007, together held in St. Jose, CA, united states in August 2007 at the side of the thirteenth ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2007.
The eight revised complete papers awarded including a close preface went via rounds of reviewing and development and have been conscientiously 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 net purposes, the rising purposes of the internet as a social medium, in addition to social community modeling and analysis.
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Extra info for Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop
8. Histogram of the number of Phase 2 threads in the 100 clusters characteristics of a focused discussion, and are there any patterns as to when and how the focused discussion can happen? The three largest threads were selected for analysis, with the ﬁrst discussing “Sustainable energy systems”, the other on “Digital entertainment” and the third on “Global travel” with 622, 405 and 776 posts respectively. To determine whether a series of posts have a focused discussion or not, we compare the content similarity as well as the number of unique contributors.
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.
Plot of averaged similarity between 10 posts in most common threads; the size of the circle indicate the inverse of number of unique contributors (the bigger the size, the lower the number of unique contributors). 03). Topic discussed include, Top: Sustainable energy systems, Middle: Digital entertainment, Bottom: Global travel. Looking for Great Ideas: Analyzing the Innovation Jam 35 4. “ ... ” 5. “It would be nice if multiple movies could play on the same screen and the audience would wear special glasses so they could see only the movie they payed for.