Advances in Social Network Mining and Analysis

Second International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008

  • Lee Giles
  • Marc Smith
  • John Yen
  • Haizheng Zhang

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5498)

Table of contents

  1. Front Matter
  2. Rumi Ghosh, Kristina Lerman
    Pages 20-35
  3. Mark Goldberg, Stephen Kelley, Malik Magdon-Ismail, Konstantin Mertsalov, William (Al) Wallace
    Pages 36-54
  4. Habiba, Yintao Yu, Tanya Y. Berger-Wolf, Jared Saia
    Pages 55-76
  5. Zhao Xu, Volker Tresp, Achim Rettinger, Kristian Kersting
    Pages 77-96
  6. Elena Zheleva, Lise Getoor, Jennifer Golbeck, Ugur Kuter
    Pages 97-113
  7. Back Matter

About these proceedings

Introduction

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.

Keywords

blog networks classification community detection computer networks data mining social networks

Editors and affiliations

  • Lee Giles
    • 1
  • Marc Smith
    • 2
  • John Yen
    • 3
  • Haizheng Zhang
    • 4
  1. 1.College of Information Science and TechnologyPennsylvania State UniversityUniversity ParkUSA
  2. 2.Microsoft Research, One Microsoft WayRedmondUSA
  3. 3.College of Information Sciences and TechnologyThe Pennsylvania State UniversityUnversity ParkUSA
  4. 4.Amazon.com.SeattleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-14929-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-14928-3
  • Online ISBN 978-3-642-14929-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book