In this paper we propose an analytic model for describing the epidemic spreading action on the one-dimensional small-world network. Based on the model, the epidemic spreading behaviors without immune strategy and with immune strategy are studied under different conditions. The obtained results...

Considering the fact that the contact strengths among people are diverse both in the duration time and the distance, in this paper, we study the epidemic dynamics with susceptible–infective–susceptible (SIS) model on a weighted adaptive network to emphasize this contact feature. In this...

Many epidemic models ignored the impact of awareness on epidemics in a population, though it is not the case from the real viewpoints. In this paper, a discrete-time SIS model with awareness interactions on degree-uncorrelated networks is considered. We study three kinds of awareness, including...

It is discovered that there are two important characters, local-world character and weight-driven character, in many real-life complex networks. Motivated by this fact, a weight-driven local-world network model is proposed, which incorporates the weight dynamics and the fixed local-world...

A review of the main results obtained by a cellular automata model recently proposed to analyze the progress of citrus variegated chlorosis epidemics is done. In this model epidemiological and environmental features, such as motility of sharpshooter vectors which perform Lévy flights, hydric...

The susceptible-infected-susceptible (SIS) epidemics in a scale-free network in which each node is a square lattice itself is investigated through large-scale computer simulations. The model combines a local contact process among individuals in a node (or city) with stochastic long-range...

Many real networks are characterized by overlapping community structures in which vertices may belong to more than one community. In this paper, we propose a network model with overlapping community structure. The analytical and numerical results show that the connectivity distribution of this...