New inequalities for network distance measures by using graph spectra

被引：4
作者：
Dehmer, Matthias [1 ]
Pickl, Stefan [1 ]
Shi, Yongtang [2 ,3 ]
Yu, Guihai [2 ,3 ,4 ]
机构：
[1] Univ Bundeswehr Munchen, Dept Comp Sci, Werner Heisenberg Weg 39, D-85577 Neubiberg, Germany
[2] Nankai Univ, Ctr Combinator, Tianjin 300071, Peoples R China
[3] Nankai Univ, LPMC, Tianjin 300071, Peoples R China
[4] Shandong Inst Business & Technol, Dept Math, Yantai 264005, Shandong, Peoples R China
来源：
DISCRETE APPLIED MATHEMATICS | 2019年 / 252卷
基金：
中国博士后科学基金;
关键词：
Analytics; Graph theory; Networks; LAPLACIAN ESTRADA INDEXES; MOLECULAR SIMILARITY; ENERGY; RADIUS; INERTIA; MODEL;
D O I：
10.1016/j.dam.2016.02.024
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Eigenvalues of graph-theoretical matrices often reflect structure properties of networks (graphs) meaningfully. In this paper, we explore inequalities for graph distance measures which are based on topological indices. Some of these indices are based on eigenvalues of graph-theoretical matrices. We here consider the adjacency matrix, the Laplacian matrix and signless Laplacian matrix. Besides proving the inequalities, we discuss the usefulness of these measures and state some conjectures. (C) 2016 Published by Elsevier B.V.
引用
收藏
页码：17 / 27
页数：11
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