Many graph invariants have been used for the construction of entropy-based measures to characterize the structure of complex networks. The starting point has been always based on assigning a probability distribution to a network when using Shannon’s entropy. In particular, Cao et al. (2014 and 2015) defined special graph entropy measures which are based on degrees powers. In this paper, we obtain some lower and upper bounds for these measures and characterize extremal graphs. Moreover we resolve one part of a conjecture stated by Cao et al
Shannon entropies for networks have been widely introduced. However, entropies for weighted graphs h...
The von Neumann entropy of a graph is a spectral complexity measure that has recently found applicat...
In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a re...
Many graph invariants have been used for the construction of entropy-based measures to characterize ...
The degree-based network entropy which is inspired by Shannon’s entropy concept becomes the informat...
The graph entropies inspired by Shannon’s entropy concept become the information-theoretic quantitie...
Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropie...
The degree-based entropy Id(G) of a graph G on m>0 edges is obtained from the well-known Shannon ent...
Abstract. Generalised degrees provide a natural bridge between local and global topological properti...
The first degree-based entropy of a graph is the Shannon entropy of its degree sequence normalized b...
In this article, we discuss the problem of establishing relations between information measures for n...
In this article, we discuss the problem of establishing relations between information measures for n...
We normalize the combinatorial Laplacian of a graph by the degree sum, look at its eigenvalues as a ...
Claude Shannon developed the concept now known as \u27Shannon entropy\u27 as a measure of uncertaint...
In last lecture we have seen an use of entropy to give a tight upper bound in number of triangles in...
Shannon entropies for networks have been widely introduced. However, entropies for weighted graphs h...
The von Neumann entropy of a graph is a spectral complexity measure that has recently found applicat...
In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a re...
Many graph invariants have been used for the construction of entropy-based measures to characterize ...
The degree-based network entropy which is inspired by Shannon’s entropy concept becomes the informat...
The graph entropies inspired by Shannon’s entropy concept become the information-theoretic quantitie...
Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropie...
The degree-based entropy Id(G) of a graph G on m>0 edges is obtained from the well-known Shannon ent...
Abstract. Generalised degrees provide a natural bridge between local and global topological properti...
The first degree-based entropy of a graph is the Shannon entropy of its degree sequence normalized b...
In this article, we discuss the problem of establishing relations between information measures for n...
In this article, we discuss the problem of establishing relations between information measures for n...
We normalize the combinatorial Laplacian of a graph by the degree sum, look at its eigenvalues as a ...
Claude Shannon developed the concept now known as \u27Shannon entropy\u27 as a measure of uncertaint...
In last lecture we have seen an use of entropy to give a tight upper bound in number of triangles in...
Shannon entropies for networks have been widely introduced. However, entropies for weighted graphs h...
The von Neumann entropy of a graph is a spectral complexity measure that has recently found applicat...
In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a re...