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Graph measures with high discrimination power revisited: A random polynomial approach

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)407-414
Number of pages8
JournalInformation Sciences
Volume467
DOIs
Publication statusPublished - 1 Oct 2018
Publication typeA1 Journal article-refereed

Abstract

Finding graph measures with high discrimination power has been triggered by searching for so-called complete graph invariants. In a series of papers, we have already investigated highly discriminating measures to distinguish graphs (networks) based on their topology. In this paper, we propose an approach where the graph measures are based on the roots of random graph polynomials. The polynomial coefficients have been defined by utilizing information functionals which capture structural information of the underlying networks. Our numerical results obtained by employing exhaustively generated graphs reveal that the new approach outperforms earlier results in the literature.

Keywords

  • Data science, Graphs, Networks, Quantitative graph theory, Statistics

Publication forum classification

Field of science, Statistics Finland