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Graph Indexing

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Managing and Mining Graph Data

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

Abstract

Advanced database systems face a great challenge arising from the emergenceof massive, complex structural data in bioinformatics, chem-informatics, busi- ness processes, etc. One of the most important functions needed in these areas is efficient search of complex graph data. Given a graph query, it is desirable to retrieve relevant graphs quickly from a large database via efficient graph indices. This chapter gives an introduction to graph substructure search, approx- imate substructure search and their related graph indexing techniques, particularly feature-based graph indexing.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of California at Santa Barbara, Santa Barbara, CA, USA

    Xifeng Yan

  2. Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States

    Jiawei Han

Authors
  1. Xifeng Yan
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  2. Jiawei Han
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Corresponding author

Correspondence to Xifeng Yan .

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Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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© 2010 Springer-Verlag US

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Yan, X., Han, J. (2010). Graph Indexing. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_5

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_5

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-6044-3

  • Online ISBN: 978-1-4419-6045-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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