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基于细江指数的图的结构分化

Structural differentiation of graphs using Hosoya-based indices.

作者信息

Dehmer Matthias, Mowshowitz Abbe, Shi Yongtang

机构信息

Department of Computer Science, Universität der Bundeswehr München, Neubiberg, Germany; Institute for Bioinformatics and Translational Research, UMIT, Hall in Tyrol, Austria.

Department of Computer Science, The City College of New York (CUNY), New York, New York, United States of America.

出版信息

PLoS One. 2014 Jul 14;9(7):e102459. doi: 10.1371/journal.pone.0102459. eCollection 2014.

DOI:10.1371/journal.pone.0102459
PMID:25019933
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4097452/
Abstract

In this paper, we introduce the Hosoya-Spectral indices and the Hosoya information content of a graph. The first measure combines structural information captured by partial Hosoya polynomials and graph spectra. The latter is a graph entropy measure which is based on blocks consisting of vertices with the same partial Hosoya polynomial. We evaluate the discrimination power of these quantities by interpreting numerical results.

摘要

在本文中，我们介绍了图的细矢谱指标和细矢信息含量。第一种度量结合了由部分细矢多项式和图谱所捕获的结构信息。后者是一种基于由具有相同部分细矢多项式的顶点组成的块的图熵度量。我们通过解释数值结果来评估这些量的区分能力。