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Graph Mining

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 213 Accesses

Synonyms

Graph pattern mining; Subgraph mining

Definition

Graph mining is defined in general as mining non-trivial graph structures from a single graph or a collection of graphs. Frequent subgraph mining is a typical example of graph mining problems, and is defined as follows. Given a graph data set D = { G 1, G 2, …, G n } where G i  = (V i , E i ) (1 ≤ i ≤ n) is a graph with a vertex set V i and an edge set E i , find all subgraphs whose support is no less than a user-specified minimum support threshold, where the support of a subgraph g is the number of graphs in D that g is subgraph isomorphic to.

Historical Background

Frequent subgraph mining was first studied by Inokuchi et al. [7], Kuramochi and Karypis [9], Yan and Han [15], and so on, with applications in chemical compound and protein structure analysis. After that, there has been extensive research in the literature that studies mining various forms of graph patterns, such as closed subgraph [16], maximal subgraph [6, 11],...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China

    Hong Cheng

  2. The Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

Authors
  1. Hong Cheng
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  2. Jeffrey Xu Yu
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Corresponding author

Correspondence to Hong Cheng .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

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© 2016 Springer Science+Business Media LLC

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Cheng, H., Yu, J.X. (2016). Graph Mining. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_80737-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_80737-1

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

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