Advances in Web Mining and Web Usage Analysis

1. Front Matter

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2. Spectral Clustering in Social Networks

   • Miklós Kurucz, András A. Benczúr, Károly Csalogány, László Lukács

   Pages 1-20

3. Looking for Great Ideas: Analyzing the Innovation Jam

   • Wojciech Gryc, Mary Helander, Rick Lawrence, Yan Liu, Claudia Perlich, Chandan Reddy et al.

   Pages 21-39

4. Segmentation and Automated Social Hierarchy Detection through Email Network Analysis

   • Germán Creamer, Ryan Rowe, Shlomo Hershkop, Salvatore J. Stolfo

   Pages 40-58

5. Mining Research Communities in Bibliographical Data

   • Osmar R. Zaïane, Jiyang Chen, Randy Goebel

   Pages 59-76

6. Dynamics of a Collaborative Rating System

   • Kristina Lerman

   Pages 77-96

7. Applying Link-Based Classification to Label Blogs

   • Smriti Bhagat, Graham Cormode, Irina Rozenbaum

   Pages 97-117

8. Why We Twitter: An Analysis of a Microblogging Community

   • Akshay Java, Xiaodan Song, Tim Finin, Belle Tseng

   Pages 118-138

9. A Recommender System Based on Local Random Walks and Spectral Methods

   • Zeinab Abbassi, Vahab S. Mirrokni

   Pages 139-153

10. Back Matter

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Thisyear’svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web’s p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.

• clustering
• collaborative rating
• data mining
• dynamics
• graph labeling
• graph mining
• knowledge
• knowledge discovery
• mathematical analysis
• modeling
• semantic web
• social network mining
• social networks
• spectral clust
• web mining

• One Microsoft Way, Redmond, USA

  Haizheng Zhang

• Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany

  Myra Spiliopoulou

• School of Computing Science, Telecommunications Information Systems, DePaul University, Chicago, USA

  Bamshad Mobasher

• College of Information Science and Technology, Pennsylvania State University, University Park, USA

  C. Lee Giles

• Department of Computer Science, University of Massachusetts, Amherst, USA

  Andrew McCallum

• Computer Engineering and Computer Science Department, University of Louisville, Louisville, USA

  Olfa Nasraoui

• Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA

  Jaideep Srivastava

• College of Information Sciences and Technology, The Pennsylvania State University, Unversity Park, USA

  John Yen

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