Interrelations of graph distance measures based on topological indices.
In this paper, we derive interrelations of graph distance measures by means of inequalities. For this investigation we are using graph distance measures based on topological indices that have not been studied in this context. Specifically, we are using the well-known Wiener index, Randić index, eigenvalue-based quantities and graph entropies. In addition to this analysis, we present results from numerical studies exploring various properties of the measures and aspects of their quality. Our results could find application in chemoinformatics and computational biology where the structural investigation of chemical components and gene networks is currently of great interest.
Department of Computer Science, Universität der Bundeswehr München, Neubiberg, Germany; Division for Bioinformatics and Translational Research, UMIT, Hall in Tyrol, Austria.
Matthias
M
Dehmer
Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine, Health and Life Sciences, Queen's University Belfast, Belfast, United Kingdom.
Frank
F
Emmert-Streib
Center for Combinatorics and LPMC-TJKLC, Nankai University, Tianjin, China; College of Computer and Control Engineering, Nankai University, Tianjin, China.
Yongtang
Y
Shi
D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D008962:Models, Theoretical