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Frequent subgraph mining in outerplanar graphs

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Abstract

In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we consider the class of outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for outerplanar graphs, and show that it works in incremental polynomial time for the practically relevant subclass of well-behaved outerplanar graphs, i.e., which have only polynomially many simple cycles. We evaluate the algorithm empirically on chemo- and bioinformatics applications.

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Authors and Affiliations

  1. Department of Computer Science III, University of Bonn, Bonn, Germany

    Tamás Horváth & Stefan Wrobel

  2. Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium

    Jan Ramon

  3. Fraunhofer Institute IAIS, Schloss Birlinghoven, Sankt Augustin, Germany

    Tamás Horváth & Stefan Wrobel

Authors
  1. Tamás Horváth
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  2. Jan Ramon
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  3. Stefan Wrobel
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Correspondence to Tamás Horváth.

Additional information

Responsible editor: M.J. Zaki.

A preliminary version of this paper appeared in: T. Eliassi-Rad, L. Ungar, M. Craven, and D. Gunopulos (Eds.), Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 197–206, ACM Press, New York, NY, 2006.

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Horváth, T., Ramon, J. & Wrobel, S. Frequent subgraph mining in outerplanar graphs. Data Min Knowl Disc 21, 472–508 (2010). https://doi.org/10.1007/s10618-009-0162-1

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