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Frequent Graph Patterns

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  • First Online:
Encyclopedia of Database Systems

  • 16 Accesses

Synonyms

Graph database mining

Definition

There are three key concepts in mining graph databases: (i) labeled graph, (ii) subgraph isomorphism, and (iii) graph support value. Based on the concepts, the problem of frequent subgraph mining could be defined in the following discussion.

Definition 1

A labeled graph G is a quadrupleG = (V, E, Σ, λ) where V is a set of vertices or nodes and E ⊆ V × V is a set of undirected edges. Σ is a set of (disjoint) vertex and edge labels, and λ: V ∪ E → Σ is a function that assigns labels to vertices and edges. Typically a total ordering is defined on the labels in Σ.

With the previous definition, a graph database is a set of labeled graphs.

Definition 2

A graph G ′ = (V′, E′, Σ′, λ′)is subgraph isomorphic to G = (V, E, Σ, λ), denoted by G ′ ⊆ G, if there exists a 1–1 mapping f: V ′ → V such that

$$ \forall v\in V^{\prime },\uplambda^{\prime }(v)=\uplambda \left(f(v)\right) $$
$$ \forall \left(u,v\right)\in E^{\prime },\left(f(u),f(v)\right)\in...

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Recommended Reading

  1. Han J, Cheng H, Xin D, Yan X. Frequent pattern mining: current status and future directions. Data Min Knowl Disc. 2007;15(1):55–86.

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  2. Huan J, Prins J, Wang W, Carter C, Dokholyan NV. Coordinated evolution of protein sequences and structures with structure entropy. Technical Reports Computer Science Department; 2006.

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  3. Huan J, Wang W, Bandyopadhyay D, Snoeyink J, Prins J, Tropsha A. Mining protein family specific residue packing patterns from protein structure graphs. In: Proceedings of the 8th Annual International Conference on Research in Computational Molecular Biology; 2004. p. 308–15.

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  4. Huan J, Wang W, Prins J. Efficient mining of frequent subgraph in the presence of isomorphism. In: Proceedings of the 3rd IEEE International Conference on Data Mining; 2003. p. 549–52.

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  5. Huan J, Wang W, Prins J, Yang J. SPIN: mining maximal frequent subgraphs from graph databases. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004. p. 581–6.

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  6. Inokuchi A, Washio T, Motoda H. An apriori-based algorithm for mining frequent substructures from graph data. In: Principles of Data Mining and Knowledge Discovery, 4th European Conference; 2000. p. 13–23.

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  7. Kuramochi M., Karypis G. Frequent subgraph discovery. In: Proceedings of the 1st IEEE International Conference on Data Mining; 2001. p. 313–20.

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  8. Nijssen S, Kok J. A quickstart in frequent structure mining can make a difference. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004.p. 647–52.

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  9. Smalter A, Huan J, Lushington G. Structure-based pattern mining for chemical compound classification. In: Proceedings of the 6th Asia Pacific Bioinformatics Conference; 2008. p. 39–48.

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  10. Vanetik N, Gudes E. Mining frequent labeled and partially labeled graph patterns. In: Proceedings of the 20th International Conference on Data Engineering; 2004. p. 91–102.

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Author information

Authors and Affiliations

  1. University of Kansas, Lawrence, KS, USA

    Jun Huan

Authors
  1. Jun Huan
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Corresponding author

Correspondence to Jun Huan .

Editor information

Editors and Affiliations

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

    Ling Liu

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

    M. Tamer Özsu

Section Editor information

  1. School of Computing Science, Simon Fraser Univ., Burnaby, British Columbia, Canada

    Jian Pei

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Huan, J. (2018). Frequent Graph Patterns. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_168

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