On efficient network similarity measures

Matthias Dehmer, Zengqiang Chen, Yongtang Shi, Y. Zhang, Shailesh Tripathi, Modjtaba Ghorbani, Abbe Mowshowitz, F. Emmert-Streib

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number124521
JournalApplied Mathematics and Computation
Volume362
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Distance measures
  • Graphs
  • Inequalities
  • Networks
  • Similarity measures

Fingerprint

Dive into the research topics of 'On efficient network similarity measures'. Together they form a unique fingerprint.

Cite this