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A Survey of Clustering Algorithms for Graph Data

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Managing and Mining Graph Data

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

Abstract

In this chapter, we will provide a survey of clustering algorithms for graph data. We will discuss the different categories of clustering algorithms and recent efforts to design clustering methods for various kinds of graphical data. Clustering algorithms are typically of two types. The first type consists of node clustering algorithms in which we attempt to determine dense regions of the graph based on edge behavior. The second type consists of structural clustering algorithms, in which we attempt to cluster the different graphs based on overall structural behavior. We will also discuss the applicability of the approach to other kinds of data such as semi-structured data, and the utility of graph mining algorithms to such representations.

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Author information

Authors and Affiliations

  1. IBM T. J. Watson Research Center, Hawthorne, NY, 10532, USA

    Charu C. Aggarwal

  2. Microsoft Research Asia, Beijing, China, 100190

    Haixun Wang

Authors
  1. Charu C. Aggarwal
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  2. Haixun Wang
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Correspondence to Charu C. Aggarwal .

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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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Aggarwal, C.C., Wang, H. (2010). A Survey of Clustering Algorithms for Graph Data. 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_9

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_9

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-6044-3

  • Online ISBN: 978-1-4419-6045-0

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