title: Recent developments in quantitative graph theory: information inequalities for networks.
abstract: In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's entropy to study the relatedness of those measures. In order to establish such information inequalities for graphs, we focus on graph entropy measures based on information functionals. To prove such relations, we use known graph classes whose instances have been proven useful in various scientific areas. Our results extend the foregoing work on information inequalities for graphs.
authors.affiliation: Institute for Bioinformatics and Translational Research, UMIT, Hall in Tyrol, Austria. matthias.dehmer@umit.at
authors.forename: Matthias
authors.initials: M
authors.lastname: Dehmer
authors.forename: Lavanya
authors.initials: L
authors.lastname: Sivakumar
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D019277:Entropy; D007256:Information Systems; D008956:Models, Chemical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated