By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen
This ebook constitutes the completely refereed post-workshop lawsuits of the ninth foreign Workshop on Mining internet 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 at the side of the thirteenth ACM SIGKDD foreign convention on wisdom Discovery and information Mining, KDD 2007.
The eight revised complete papers provided including a close preface went via rounds of reviewing and development and have been conscientiously chosen from 23 preliminary submisssions. the improved papers tackle all present concerns in net 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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Extra 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
The Enron Corporation’s email collection described in section 2, is a publicly available set of private corporate data released during the judicial proceedings against the Enron corporation. Several researchers have explored it mostly from a Natural Language Processing (NLP) perspective [19,21,24]. Social network analysis (SNA) examining structural features  has also been applied to extract properties of the Enron network and attempts to detect the key players around the time of Enron’s crisis;  studied the patterns of communication of Enron employees diﬀerentiated by their hierarchical level;  interestingly enough found that word use changed according to the functional position, while  conducted a thread analysis to ﬁnd out employees’ responsiveness.
The social network structure of threads and the whole Jam. Within each thread, we can analyze the structure of the discussion, and collect statistics such as how many “leaves” (postings with no response) there were, how deep is the typical discussion in the thread, etc. g, messages per poster). 3. The organizational relationships between the contributors. Since the vast majority of contributors were IBM employees, we can make use of the online directory of worldwide IBM employees (known as Blue Pages), to capture the organizational and hierarchical relationships between the contributors in each thread, in each Big Idea, etc.
Determine n diﬀerent levels (or echelons) of social hierarchy within which to place all the users. This is a clustering step, and n can be bounded. The rankings, groups and echelons are used to reconstruct an organization chart as accurately as possible. To compute S , we must ﬁrst scale and normalize 46 G. Creamer et al. each of the previous statistics which we have gathered. The contribution, C, of each metric is individually mapped to a [0, 100] scale and weighted with the following formula: wx · Cx = wx · 100 · xi − inf x sup x − inf x where x is the metric in question, wx is the respective weight for that metric, the sup x and inf x are computed across all i users and xi is the value for the user.