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Graph automorphism perception algorithms in computer-enhanced structure elucidation

M Razinger1, K Balasubramanian, M E Munk

  • 1Department of Chemistry, Arizona State University, Tempe 85287-1604.

Journal of Chemical Information and Computer Sciences
|March 1, 1993
PubMed
Summary
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This study introduces an algorithm to construct graph automorphism groups from vertex partitions, aiding in understanding graph symmetry. The Shelley-Munk algorithm is highlighted for its effectiveness in computer-aided structure elucidation.

Area of Science:

  • Graph theory
  • Computational chemistry
  • Computer-aided structure elucidation

Background:

  • Graph symmetry is fundamentally defined by the vertex automorphism group.
  • The vertex automorphism group is a subgroup of the complete vertex permutation group.
  • Automorphism groups can be derived from vertex automorphism partitions.

Purpose of the Study:

  • To describe an algorithm for constructing graph automorphism groups from vertex partitions.
  • To evaluate the utility of this algorithm for graphs with multiple vertex partition sets.
  • To compare existing topological symmetry perception algorithms.

Main Methods:

  • Development of an algorithm to construct the automorphism group from vertex partitioning.
  • Comparison of several topological symmetry perception algorithms.

Related Experiment Videos

  • Evaluation of algorithms within the SESAMI system framework.

    • Main Results:

    • The described algorithm effectively constructs graph automorphism groups.
    • The algorithm is particularly useful for graphs with complex vertex partitioning.
    • The Shelley-Munk algorithm demonstrated superior performance in generating automorphism partitions.

    Conclusions:

    • The proposed algorithm provides a method for determining graph automorphism groups.
    • The Shelley-Munk algorithm is recommended for computer-enhanced structure elucidation due to its efficiency in yielding automorphism partitions.