@incollection{Fischer2008Subgr-5922,
  year={2008},
  title={Subgraph Mining},
  edition={2},
  publisher={IGI Global},
  address={Hershey, PA, USA},
  booktitle={Encyclopedia of Data Warehousing and Mining},
  pages={1865--1870},
  author={Fischer, Ingrid and Meinl, Thorsten}
}

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    <dcterms:bibliographicCitation>First publ. in: Encyclopedia of Data Warehousing and Mining, 2. ed. - Hershey, PA, USA : IGI Global, 2008, pp. 1865-1870</dcterms:bibliographicCitation>
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    <dc:contributor>Fischer, Ingrid</dc:contributor>
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    <dc:creator>Meinl, Thorsten</dc:creator>
    <dc:contributor>Meinl, Thorsten</dc:contributor>
    <dcterms:title>Subgraph Mining</dcterms:title>
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    <dcterms:abstract xml:lang="eng">Graphs are often used as models in very different application areas ranging from networks to molecules and proteins. Having graphs in a graph database it is an interesting problem to find small graph parts, so called subgraphs, that appear in a certain number of graphs within the database. Possible subgraphs of a set of graphs form a lattice that must be searched to find the subgraphs that appear most frequently. Two steps are necessary for this search: first new possible subgraphs must be generated, secondly it must be checked how often a newly generated subgraph appears in the database. Additionally intelligent pruning methods, inexact graph matching and background knowledge can be incorporated in the mining algorithms.</dcterms:abstract>
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