Advertisement

Managing and Mining Graph Data

  • Charu C. Aggarwal
  • Haixun Wang

Part of the Advances in Database Systems book series (ADBS, volume 40)

Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Charu C. Aggarwal, Haixun Wang
    Pages 1-11
  3. Deepayan Chakrabarti, Christos Faloutsos, Mary McGlohon
    Pages 69-123
  4. Huahai He, Ambuj K. Singh
    Pages 125-160
  5. Xifeng Yan, Jiawei Han
    Pages 161-180
  6. Jeffrey Xu Yu, Jiefeng Cheng
    Pages 181-215
  7. Kaspar Riesen, Xiaoyi Jiang, Horst Bunke
    Pages 217-247
  8. Haixun Wang, Charu C. Aggarwal
    Pages 249-273
  9. Charu C. Aggarwal, Haixun Wang
    Pages 275-301
  10. Victor E. Lee, Ning Ruan, Ruoming Jin, Charu Aggarwal
    Pages 303-336
  11. Koji Tsuda, Hiroto Saigo
    Pages 337-363
  12. Hong Cheng, Xifeng Yan, Jiawei Han
    Pages 365-392
  13. Xintao Wu, Xiaowei Ying, Kun Liu, Lei Chen
    Pages 421-453
  14. Debora Donato, Aristides Gionis
    Pages 455-485
  15. Lei Tang, Huan Liu
    Pages 487-513
  16. Frank Eichinger, Klemens Bo̶hm
    Pages 515-546
  17. S. Parthasarathy, S. Tatikonda, D. Ucar
    Pages 547-580
  18. Nikil Wale, Xia Ning, George Karypis
    Pages 581-606
  19. Back Matter
    Pages 607-610

About this book

Introduction

Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing.

Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science.

About the Editors:

Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has worked as a researcher at IBM since then, and has published over 130 papers in major data mining conferences and journals. He has applied for or been granted over 70 US and International patents, and has thrice been designated a Master Inventor at IBM. He has received an IBM Corporate award for his work on data stream analytics, and an IBM Outstanding Innovation Award for his work on privacy technology. He has served on the executive committees of most major data mining conferences. He has served as an associate editor of the IEEE TKDE, as an associate editor of the ACM SIGKDD Explorations, and as an action editor of the DMKD Journal. He is a fellow of the IEEE, and a life-member of the ACM.

Haixun Wang is currently a researcher at Microsoft Research Asia. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He subsequently worked as a researcher at IBM until 2009. His main research interest is database language and systems, data mining, and information retrieval. He has published more than 100 research papers in referred international journals and conference proceedings. He serves as an associate editor of the IEEE TKDE, and has served as a reviewer and program committee member of leading database conferences and journals.

Keywords

Clustering Graph Information Retrieval Managing Mining algorithm biological datasets chemical graph data classification currentjm data mining graph data graph mining query language social networks

Editors and affiliations

  • Charu C. Aggarwal
    • 1
  • Haixun Wang
    • 2
  1. 1.Thomas J. Watson Research CenterIBMHawthorneU.S.A.
  2. 2.Microsoft Research AsiaBeijingChina, People's Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-6045-0
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-1-4419-6044-3
  • Online ISBN 978-1-4419-6045-0
  • Series Print ISSN 1386-2944
  • Buy this book on publisher's site