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

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 booklet constitutes the completely refereed post-workshop court cases of the ninth overseas Workshop on Mining internet facts, 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 overseas 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 rigorously chosen from 23 preliminary submisssions. the improved papers tackle all present concerns in internet mining and social community research, together with conventional internet 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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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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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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2) Phase 2 to Finalist ideas: we further examine the topic overlapped between the threads in Phase 2 and those selected as successful finalist ideas by going through the clusters with the most threads from Phase 2. From Table 2, we can see an impressively direct mapping from the top-ranked clusters (by the number of threads from Phase 2) to the finalist. For example, the cluster with the largest number of threads from Phase 2 is shown in the first line. e. “Electronic Health Record System”. Another example is the cluster devoted to the idea of “Digital Me”.

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. Group users which have similar social scores and clique connectivity iii. Determine n different levels (or echelons) of social hierarchy within which to place all the users.

Different, more sophisticated, ranking and grouping algorithms have been considered and will be implemented, and will be discussed in the following section on future work. When a node is selected with the mouse, all users connected to the selected user through cliques are highlighted and the user, time score and social score populate a small table at the bottom of the interface for inspection. Nodes can be individually picked or picked as groups and rearranged at the user’s discretion. If the organization is not accurate or has misrepresented the structure of the actual social hierarchy in question, the user can return to the analysis window and adjust the weights in order to emphasize importance in the correct individuals and then can recreate the visualization.

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