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Learning Closed Sets of Labeled Graphs for Chemical Applications

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Inductive Logic Programming (ILP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3625))

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Abstract

Similarity of graphs with labeled vertices and edges is naturally defined in terms of maximal common subgraphs. To avoid computation overload, a parameterized technique for approximation of graphs and their similarity is used. A lattice-based method of binarizing labeled graphs that respects the similarity operation on graph sets is proposed. This method allows one to compute graph similarity by means of algorithms for computing closed sets. Results of several computer experiments in predicting biological activity of chemical compounds that employ the proposed technique testify in favour of graph approximations as compared to complete graph representations: gaining in efficiency one (almost) does not lose in accuracy.

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Author information

Authors and Affiliations

  1. All-Russia Institute for Scientific and Technical Information (VINITI), Moscow, Russia

    Sergei O. Kuznetsov & Mikhail V. Samokhin

Authors
  1. Sergei O. Kuznetsov
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  2. Mikhail V. Samokhin
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Editor information

Editors and Affiliations

  1. Institut für Informatik I12, Technische Universität München, Boltzmannstr. 3, D-85748, Garching b. München, Germany

    Stefan Kramer

  2. Department of Computer Science, University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

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Kuznetsov, S.O., Samokhin, M.V. (2005). Learning Closed Sets of Labeled Graphs for Chemical Applications. In: Kramer, S., Pfahringer, B. (eds) Inductive Logic Programming. ILP 2005. Lecture Notes in Computer Science(), vol 3625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536314_12

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  • DOI: https://doi.org/10.1007/11536314_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28177-1

  • Online ISBN: 978-3-540-31851-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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