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Efficient Algorithms for Node Disjoint Subgraph Homeomorphism Determination

  • Yanghua Xiao
  • Wentao Wu
  • Wei Wang
  • Zhenying He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4947)

Abstract

Recently, great efforts have been dedicated to researches on the management of large-scale graph-based data, where node disjoint subgraph homeomorphism relation between graphs has been shown to be more suitable than (sub)graph isomorphism in many cases, especially in those cases where node skipping and node mismatching are desired. However, no efficient algorithm for node disjoint subgraph homeomorphism determination (ndSHD) has been available. In this paper, we propose two computationally efficient ndSHD algorithms based on state spaces searching with backtracking, which employ many heuristics to prune the search spaces. Experimental results on synthetic data sets show that the proposed algorithms are efficient, require relatively little time in most of cases, can scale to large or dense graphs, and can accommodate to more complex fuzzy matching cases.

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References

  1. 1.
    Kelley, R.B., et al.: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. PNAS 100(20), 11394–11399 (2003)CrossRefGoogle Scholar
  2. 2.
    Sharan, R., et al.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. In: RECOMB 2004, pp. 282–289 (2004)Google Scholar
  3. 3.
    Garey, M.R., Johnson, D.S.: Computers and Intractability. A Guide to the Theory of NP-completeness. W.H. Freeman and Company, New York (2003)Google Scholar
  4. 4.
    Robertson, N., Seymour, P.D.: Graph minors. XIII: The disjoint paths problem. Journal of Combinatorial Theory 63, 65–110 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    IIIya, V.: Hicks:Branch Decompositions and Minor Containment. Networks 43(1), 1–9 (2004)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Ullmann, J.R.: An Algorithm for Subgraph Isomorphism. Journal of the ACM 23, 31–42 (1976)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Diestel, R.: Graph Theory. Springer, Heidelberg (2000)Google Scholar
  8. 8.
    Jin, R., Wang, C., Polshakov, D., Parthasarathy, S., Agrawal, G.: Discovering frequent topological structures from graph datasets. In: KDD 2005, Chicago,USA, pp. 606–611 (2005)Google Scholar
  9. 9.
    Nilsson, N.J.: Principles of Artificial Intelligence. Springer, Heidelberg (1982)zbMATHGoogle Scholar
  10. 10.
    Erdös, P., Rényi, A.: On random graphs. Publicationes Mathematicae, 290–297 (1959)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yanghua Xiao
    • 1
  • Wentao Wu
    • 1
  • Wei Wang
    • 1
  • Zhenying He
    • 1
  1. 1.Department of Computing and Information TechnologyFuDan UniversityShangHaiChina

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