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Structural Discrimination of Networks by Using Distance, Degree and Eigenvalue-Based Measures

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  • Matthias Dehmer
  • Martin Grabner
  • Boris Furtula

Abstract

In chemistry and computational biology, structural graph descriptors have been proven essential for characterizing the structure of chemical and biological networks. It has also been demonstrated that they are useful to derive empirical models for structure-oriented drug design. However, from a more general (complex network-oriented) point of view, investigating mathematical properties of structural descriptors, such as their uniqueness and structural interpretation, is also important for an in-depth understanding of the underlying methods. In this paper, we emphasize the evaluation of the uniqueness of distance, degree and eigenvalue-based measures. Among these are measures that have been recently investigated extensively. We report numerical results using chemical and exhaustively generated graphs and also investigate correlations between the measures.

Suggested Citation

  • Matthias Dehmer & Martin Grabner & Boris Furtula, 2012. "Structural Discrimination of Networks by Using Distance, Degree and Eigenvalue-Based Measures," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-15, July.
    • Handle: RePEc:plo:pone00:0038564
    DOI: 10.1371/journal.pone.0038564
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    Cited by:

    1. Dehmer, Matthias & Shi, Yongtang & Mowshowitz, Abbe, 2015. "Discrimination power of graph measures based on complex zeros of the partial Hosoya polynomial," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 352-355.

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