The degree-based network entropy which is inspired by Shannon’s entropy concept becomes the information-theoretic quantity for measuring the structural information of graphs and complex networks. In this paper, we study some properties of the degree-based network entropy. Firstly we develop a refinement of Jensen’s inequality. Next we present the new and more accurate upper bound and lower bound for the degree-based network entropy only using the order, the size, the maximum degree and minimum degree of a network. The bounds have desirable performance to restrict the entropy in different kinds of graphs. Finally, we show an application to structural complexity analysis of a computer network modeled by a connected graph
We normalize the combinatorial Laplacian of a graph by the degree sum, look at its eigenvalues as a ...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...
It is often claimed that the entropy of a network's degree distribution is a proxy for its robustnes...
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...
Many graph invariants have been used for the construction of entropy-based measures to characterize ...
The degree-based entropy Id(G) of a graph G on m>0 edges is obtained from the well-known Shannon ent...
Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropie...
Abstract. Generalised degrees provide a natural bridge between local and global topological properti...
In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a re...
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...
It is often claimed that the entropy of a network’s degree distribution is a proxy for its robustnes...
One of the most popular methods of estimating the complexity of networks is to measure the entropy o...
Entropy is an important indicator to measure network heterogeneity. We propose a new network structu...
We normalize the combinatorial Laplacian of a graph by the degree sum, look at its eigenvalues as a ...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...
It is often claimed that the entropy of a network's degree distribution is a proxy for its robustnes...
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...
Many graph invariants have been used for the construction of entropy-based measures to characterize ...
The degree-based entropy Id(G) of a graph G on m>0 edges is obtained from the well-known Shannon ent...
Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropie...
Abstract. Generalised degrees provide a natural bridge between local and global topological properti...
In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a re...
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...
It is often claimed that the entropy of a network’s degree distribution is a proxy for its robustnes...
One of the most popular methods of estimating the complexity of networks is to measure the entropy o...
Entropy is an important indicator to measure network heterogeneity. We propose a new network structu...
We normalize the combinatorial Laplacian of a graph by the degree sum, look at its eigenvalues as a ...
A graph’s entropy is a functional one, based on both the graph itself and the distribution of probab...
It is often claimed that the entropy of a network's degree distribution is a proxy for its robustnes...