The degree-based entropy Id(G) of a graph G on m>0 edges is obtained from the well-known Shannon entropy −∑i=1np(xi)logp(xi) in information theory by replacing the probabilities p(xi) by the fractions [Formula presented], where {v1,v2,…,vn} is the vertex set of G, and dG(vi) is the degree of vi. We continue earlier work on Id(G). Our main results deal with the effect of a number of graph operations on the value of Id(G). We also illustrate the relevance of these results by applying some of these operations to prove a number of extremal results for the degree-based entropy of trees and unicyclic graphs
Shannon entropy is an information-theoretic measure of unpredictability in probabilistic models. Rec...
A topological index is a number that is connected to a chemical composition in order to correlate a ...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...
The graph entropies inspired by Shannon’s entropy concept become the information-theoretic quantitie...
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 first degree-based entropy of a graph is the Shannon entropy of its degree sequence normalized b...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...
Claude Shannon developed the concept now known as \u27Shannon entropy\u27 as a measure of uncertaint...
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...
Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropie...
We normalize the combinatorial Laplacian of a graph by the degree sum, look at its eigenvalues as a ...
In last lecture we have seen an use of entropy to give a tight upper bound in number of triangles in...
Abstract. Generalised degrees provide a natural bridge between local and global topological properti...
Shannon entropy is an information-theoretic measure of unpredictability in probabilistic models. Rec...
A topological index is a number that is connected to a chemical composition in order to correlate a ...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...
The graph entropies inspired by Shannon’s entropy concept become the information-theoretic quantitie...
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 first degree-based entropy of a graph is the Shannon entropy of its degree sequence normalized b...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...
Claude Shannon developed the concept now known as \u27Shannon entropy\u27 as a measure of uncertaint...
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...
Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropie...
We normalize the combinatorial Laplacian of a graph by the degree sum, look at its eigenvalues as a ...
In last lecture we have seen an use of entropy to give a tight upper bound in number of triangles in...
Abstract. Generalised degrees provide a natural bridge between local and global topological properti...
Shannon entropy is an information-theoretic measure of unpredictability in probabilistic models. Rec...
A topological index is a number that is connected to a chemical composition in order to correlate a ...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...