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On efficient network similarity measures

Author

Listed:
  • Dehmer, Matthias
  • Chen, Zengqiang
  • Shi, Yongtang
  • Zhang, Yusen
  • Tripathi, Shailesh
  • Ghorbani, Modjtaba
  • Mowshowitz, Abbe
  • Emmert-Streib, Frank

Abstract

This paper presents novel graph similarity measures which can be applied to simple directed and undirected networks. To define the graph similarity measures, we first map graphs to real numbers by utilizing structural graph measures. Then, we define measures of similarity between real numbers and prove that they can be used as proxies for graph similarity. Numerical results are derived to show the domain coverage of these measures as well as their clustering ability. The latter relates to the efficient grouping of graphs according to certain structural properties. Our numerical results are sensitive to these properties and offer insights useful for designing effective graph similarity measures.

Suggested Citation

  • Dehmer, Matthias & Chen, Zengqiang & Shi, Yongtang & Zhang, Yusen & Tripathi, Shailesh & Ghorbani, Modjtaba & Mowshowitz, Abbe & Emmert-Streib, Frank, 2019. "On efficient network similarity measures," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    • Handle: RePEc:eee:apmaco:v:362:y:2019:i:c:23
    DOI: 10.1016/j.amc.2019.06.035
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    References listed on IDEAS

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    1. Dehmer, M. & Moosbrugger, M. & Shi, Y., 2015. "Encoding structural information uniquely with polynomial-based descriptors by employing the Randić matrix," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 164-168.
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