By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum
This booklet constitutes the completely refereed post-workshop lawsuits of the ninth overseas 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 along with the thirteenth ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2007.
The eight revised complete papers awarded 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 net and semantic net functions, the rising functions of the net 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
There has been tremendous work on the social network study over the past century [14,2,11]. Nowadays commoditization and globalization are dominant themes having a major impact on business execution. As a result, large companies are focusing extensively on innovation as a signiﬁcant driver of the new ideas necessary to remain competitive in this evolving business climate. Of course, the broader issue is how does a company foster innovation, and speciﬁcally how do we identify, extend, and capitalize on the new ideas that are created?
1348557 H. Zhang et al. ): WebKDD/SNA-KDD 2007, LNCS 5439, pp. 21–39, 2009. c Springer-Verlag Berlin Heidelberg 2009 22 W. Gryc et al. IBM recently introduced an online information forum or “Innovation Jam” [9,13] where employees (and, in some cases, external participants) are encouraged to share their ideas on pre-selected topics of broad interest. Analysis of the information collected in such forums requires a number of advanced data processing steps including extraction of dominant, recurring themes and ultimately characterization of the degree of innovation represented by the various discussion threads created in the forum.
In the succeeding sections we will describe the data itself and our analytical approaches. Since a prevalent hypothesis is that a major advantage of the Jam is that it brings together people from diﬀerent parts of IBM, and diﬀerent geographical locations, who would otherwise be unlikely to interact — and that such interactions between diverse groups are likely to lead to new insights and innovations — this data is of particular interest in our analysis. These data sources are: 1. The text of the threads itself.
Advances in Web Mining and Web Usage Analysis, 9 conf., WebKDD 2007 by Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum