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Efficient Method to Perform Isomorphism Testing of Labeled Graphs

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3984)

Abstract

The need to perform isomorphism testing is emerging recently in many application domains such as graph-based data mining for discovering frequent common patterns in a graph database. Due to the complex nature of graph representations, the isomorphism testing between labeled graphs is one of the most time-consuming phases during the mining process. The canonical form of a graph that serves as the isomorphism certificate needs O(n!) to produce for a graph of order n, or Θ(Π i = 1 c(|π i |!)) if vertex invariants are employed to divide n vertices into c equivalence classes with |π i | vertices in each class i. In this paper, we propose a new algorithm to perform isomorphism testing of labeled graphs with worst case time complexity O i = 1 c(|π i |!)), in which the product of all |π i |! terms is replaced by the sum of the terms and the asymptotic notation is changed from big theta to big oh. To the best of our knowledge, this proposed model is the latest work that focuses on the dealing of the isomorphism testing of labeled graphs. The result of this algorithm is directly applicable to the fields of graph isomorphism testing for labeled graphs.

Keywords

  • Large Neighbor
  • Label Graph
  • Edge Label
  • Graph Isomorphism
  • Feasibility Testing

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2006 Springer-Verlag Berlin Heidelberg

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Hsieh, SM., Hsu, CC., Hsu, LF. (2006). Efficient Method to Perform Isomorphism Testing of Labeled Graphs. In: , et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751649_46

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  • DOI: https://doi.org/10.1007/11751649_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34079-9

  • Online ISBN: 978-3-540-34080-5

  • eBook Packages: Computer ScienceComputer Science (R0)