By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum
This booklet constitutes the completely refereed post-workshop lawsuits of the ninth foreign Workshop on Mining internet facts, 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 together with the thirteenth ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2007.
The eight revised complete papers offered 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 concerns in internet mining and social community research, together with conventional net and semantic net functions, the rising functions of the internet as a social medium, in addition to social community modeling and analysis.
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Additional info for Advances in Web Mining and Web Usage Analysis, 9 conf., WebKDD 2007
T6 Mean of the number of messages for all questions 2 . T7 Standard deviation of the number of messages for all questions 2 . T8 Weighted number of overlapping contributors involved in other big ideas. C1 Mean of the pairwise cosine similarity scores between the threads 3 . C2 Standard deviation of the pairwise scores between the threads 3 . C3 Total number of pairwise scores between all threads. C4 Maximum pairwise score between the threads. C5 Minimum pairwise score between the threads. O1 Average pairwise distance between the contributors within a big idea 4 .
We used the recursive algorithm proposed by  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. The breakdown of social scores is then used to: i. Rank users from most important to least important ii.
As a ﬁrst consideration we estimate the average probability of a repeating contributor to post to a new thread, where new is deﬁned as a thread to which he has never posted. We deﬁne this probability for a given contributes as the number of contributed posts minus one (the ﬁrst posting is by deﬁnition to a thread that the contributer has not posted in before) divided by the number of diﬀerent threads in which he posted. And indeed, this probability is surprisingly high at 62%. The histogram in Figure 7 shows that a large number of Jam contributors ventured into multiple threads.
Advances in Web Mining and Web Usage Analysis, 9 conf., WebKDD 2007 by Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum