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A Survey of Algorithms for Dense Subgraph Discovery

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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 present a survey of algorithms for dense subgraph discovery.The problem of dense subgraph discovery is closely related to clustering though the two problems also have a number of differences. For example, the problem of clustering is largely concerned with that of finding a fixed partition in the data, whereas the problem of dense subgraph discovery defines these dense components in a much more flexible way. The problem of dense subgraph discovery may wither be defined over single or multiple graphs. We explore both cases. In the latter case, the problem is also closely related to the problem of the frequent subgraph discovery. This chapter will discuss and organize the literature on this topic effectively in order to make it much more accessible to the reader.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Kent State University, Kent, OH, 44242, USA

    Victor E. Lee, Ning Ruan & Ruoming Jin

  2. IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA

    Charu Aggarwal

Authors
  1. Victor E. Lee
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  2. Ning Ruan
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  3. Ruoming Jin
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  4. Charu Aggarwal
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Correspondence to Victor E. Lee .

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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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Lee, V.E., Ruan, N., Jin, R., Aggarwal, C. (2010). A Survey of Algorithms for Dense Subgraph Discovery. 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_10

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_10

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  • Online ISBN: 978-1-4419-6045-0

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