Download PDF by Miklós Kurucz, András A. Benczúr (auth.), Haizheng Zhang,: Advances in Web Mining and Web Usage Analysis: 9th

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.)

ISBN-10: 3642005276

ISBN-13: 9783642005275

ISBN-10: 3642005284

ISBN-13: 9783642005282

This e-book constitutes the completely refereed post-workshop complaints of the ninth overseas Workshop on Mining net 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 side 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 conscientiously chosen from 23 preliminary submisssions. the improved papers handle all present matters in internet mining and social community research, together with conventional net and semantic net purposes, the rising functions of the internet as a social medium, in addition to social community modeling and analysis.

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Read Online 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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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 on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers

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In Section 2, we discuss some metrics that have been used to characterize the structure of social networks formed via other kinds of discussion groups. Section 3 describes the specifics of the IBM Innovation Jam and the collected data. Section 4 summarizes some key aspects of the dynamics of the Jam interactions. Finally, Sections 5 and 6 describe respectively the unsupervised and supervised learning approaches we have applied to this data. 2 Related Work The Innovation Jam is a unique implementation of threaded discussions, whereby participants create topics and explicitly reply to each other using a “reply to” button.

Of Threads with ≤10 responses 2673 60 No. of Threads with ≥100 responses 56 12 26 W. Gryc et al. Percentage of Messages in Each Forum 45 40 40 Percentage 35 32 30 25 23 22 23 20 18 20 22 Phase 1 Phase 2 15 10 5 0 A Better Planet Finance & Commerce Going Places Staying Healthy Fig. 2. Percentage of messages in each forum for Phase 1 and Phase 2 Percentage Percentage of Contributors 50 45 40 35 30 25 20 15 10 5 0 Phase 1 Phase 2 1 2 3 4 5 6 7 8 9 10 Number of Responses Fig. 3. Percentage of contributors who responded more than 1-20 times during Phase 1 and Phase 2 1 and 3640 in Phase 2, it is interesting to note that these percentages are very similar for both phases.

3 SNA Algorithm The social network analysis algorithm works as follows: For each email user in the dataset analyze and calculate several statistics for each feature of each user. The individual features are normalized and used in a probabilistic framework with which users can be measured against one another for the purposes of ranking and grouping. It should be noted that the list of email users in the dataset represents a wide array of employee positions within the organization or across organizational departments.

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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 Miklós Kurucz, András A. Benczúr (auth.), Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen (eds.)


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