Networking Internet

Advances in Web Mining and Web Usage Analysis, 9 conf., by Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee

By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum

This ebook 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 at the side of the thirteenth ACM SIGKDD overseas 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 deal with all present concerns in internet mining and social community research, together with conventional internet and semantic net purposes, the rising purposes of the internet as a social medium, in addition to social community modeling and analysis.

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Removing the most frequent words and those appearing less than 2 times in the whole collection. These processes results in a vocabulary of 10945. Then we convert the thread-level documents into the feature vectors using the “ltc” TF-IDF term weighting [4]. e. the topics that discussed in Phase 1 have been picked up by Phase 2, which can be seen as a potential indicator of “finalists” of ideas for funding. Therefore when we cluster the threads from Phase 1 and Phase 2, an optimal case is that we can find three types of clusters: (1) the clusters that mostly consist of threads in Phase 1 (2) those mostly composed of threads in Phase 2; and (3) the clusters with the threads in both phases, which help us examine if they are the potential finalists for funding.

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.

Our approach determines link mining metrics which can reproduce approximate social hierarchy within an organization or a corporate household, and rank its members. We use our metric to analyze email flows within an organization to extract social hierarchy. We analyze the behavior of the 42 G. Creamer et al. communication patterns without having to take into account the actual contents of the email messages. By performing behavior analysis and determining the communication patterns we are able to automatically: – Rank the major officers of an organization.

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