Advances in Social Network Mining and Analysis

1. Front Matter

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2. Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study

   • Brian Gallagher, Tina Eliassi-Rad

   Pages 1-19

3. Community Detection Using a Measure of Global Influence

   • Rumi Ghosh, Kristina Lerman

   Pages 20-35

4. Communication Dynamics of Blog Networks

   • Mark Goldberg, Stephen Kelley, Malik Magdon-Ismail, Konstantin Mertsalov, William (Al) Wallace

   Pages 36-54

5. Finding Spread Blockers in Dynamic Networks

   • Habiba, Yintao Yu, Tanya Y. Berger-Wolf, Jared Saia

   Pages 55-76

6. Social Network Mining with Nonparametric Relational Models

   • Zhao Xu, Volker Tresp, Achim Rettinger, Kristian Kersting

   Pages 77-96

7. Using Friendship Ties and Family Circles for Link Prediction

   • Elena Zheleva, Lise Getoor, Jennifer Golbeck, Ugur Kuter

   Pages 97-113

8. Information Theoretic Criteria for Community Detection

   • L. Karl Branting

   Pages 114-130

9. Back Matter

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This year’s volume of Advances in Social Network Analysis contains the p- ceedings for the Second International Workshop on Social Network Analysis (SNAKDD 2008). The annual workshop co-locates with the ACM SIGKDD - ternational Conference on Knowledge Discovery and Data Mining (KDD). The second SNAKDD workshop was held with KDD 2008 and received more than 32 submissions on social network mining and analysis topics. We accepted 11 regular papers and 8 short papers. Seven of the papers are included in this volume. In recent years, social network research has advanced signi?cantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale o?ine social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are - creasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topol- ical properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining socialnetworks.These issues haveimportant implications oncom- nitydiscovery,anomalydetection,trendpredictionandcanenhanceapplications in multiple domains such as information retrieval, recommendation systems, - curity and so on.

• blog networks
• classification
• community detection
• computer networks
• data mining
• social networks

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

  Lee Giles

• Microsoft Research, One Microsoft Way, Redmond, USA

  Marc Smith

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

  John Yen

• Amazon.com., Seattle, USA

  Haizheng Zhang

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