By Miklós Kurucz, András A. Benczúr (auth.), Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen (eds.)
This ebook constitutes the completely refereed post-workshop court cases of the ninth overseas Workshop on Mining internet info, 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 deal with all present matters in internet mining and social community research, together with conventional internet and semantic net functions, the rising purposes of the internet as a social medium, in addition to social community modeling and analysis.
Read or Download Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers PDF
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Additional resources for Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers
By performing behavior analysis and determining the communication patterns we are able to automatically: – Rank the major oﬃcers of an organization. – Group similarly ranked and connected users in order to accurately reproduce the organizational structure in question. – Understand relationship strengths between speciﬁc segments of users. This work is a natural extension of previous work on the Email Mining Toolkit project (EMT) [32,33]. New functionality has been introduced into the EMT system for the purposes of automatically extracting social hierarchy information from any email collection.
The ISI treatment of the Enron corpus consisted of deleting extraneous, unneeded emails and ﬁxing some anomalies in the collection data having to do with empty or illegal user email names and bounced emails messages. In addition duplicates and blank emails were removed. We also used a suplementary ﬁle provided by  to assign the position of each user. 5% of the users are classiﬁed as “employee” or “N/A”. The classiﬁcation “employee” does not bring any additional information more than indicating that the user is formally working at Enron.
The resulting social Jam network is shown for a number of points in time (2 hours, 3 hours, 4 hours and 10 hours after start of Jam) in Figure 5. The graph Jam Phase Posts per Hour 1800 1600 1400 Number of Posts 1200 1000 800 600 400 200 0 0 10 20 30 40 50 Hours since Jam Start at 10AM EST 60 70 80 Fig. 4. Number of postings over time during Jam Phase 1 28 W. Gryc et al. Pajek Pajek Pajek Pajek Fig. 5. Evolvement of the social Jam network for 2, 3, 4 and 10 hours after the Phase 1 start layouts are generated using Pajek .