Network architecture design is critical for optimizing industrial networks. Network
architectures can be classified into small-scale networks and large-scale networks based on …

Entropy is an important indicator to measure network heterogeneity. We propose a new
network structure entropy, SP (series-parallel) structure entropy, based on the global …

This paper elaborates on the Random Network Model (RNM) as a mathematical framework
for modelling and analyzing the generation of complex networks. Such framework allows the …

Complex networks have been applied to model numerous interactive nonlinear systems in
the real world. Knowledge about network topology is crucial to an understanding of the …

Network Science defines complex systems as objects interacting in a network with nodes
and edges. Stochastic network models that treat networks as a collection of nodes with fixed …

Entropy is an important index for describing the structure, function, and evolution of network.
The existing research on entropy is primarily applied to undirected networks. Compared with …

The heterogeneity of scale-free networks is studied using the network structure entropy
(NSE). The NSE of scale-free networks is presented analytically by introducing the degree …

Real-world networks such as the Internet and WWW have many common traits. Until now,
hundreds of models were proposed to characterize these traits for understanding the …

Time evolving Random Network Models are presented as a mathematical framework for
modelling and analyzing the evolution of complex networks. This framework allows the …

The network structure entropy has served as one of the index measuring network
heterogeneity, but it gives no considerations to the impact of isolated nodes on the network …