Yin Yan-Ping

Multiple Partial Attacks on Complex Networks

We numerically investigate the effect of four kinds of partial attacks of multiple targets on the Barabasi-Albert (BA) scale-free network and the Erdos-Renyi (ER) random network. Comparing with the effect of single target complete knockout we find that partial attacks of multiple targets may produce an effect higher than the complete knockout of a single target on both BA scale-free network and ER random network. We also find that the BA scale-free network seems to be more susceptible to multi-target partial attacks than the ER random network.

Continuous Weight Attack on Complex Network

We introduce a continuous weight attack strategy and numerically investigate the effect of continuous weight attack strategy on the Barabasi–Albert (BA) scale-free network and the Erdos–Renyi (ER) random network. We use a weight coefficient ω to define the attack intensity. The weight coefficient ω increases continuously from 1 to infinity, where 1 represents no attack and infinity represents complete destructive attack. Our results show that the continuous weight attack on two selected nodes with small ω (ω ≈ 3) could achieve the same damage of complete elimination of a single selected node on both BA and ER networks. It is found that the continuous weight attack on a single selected edge with small ω (ω ≈ 2) can reach the same effect of complete elimination of a single edge on BA network, but on ER network the damage of the continuous weight attack on a single edge is close to but always smaller than that of complete elimination of edge even if ω is very large.

Spatiotemporal Characteristic of Epidemic Spreading in Mobile Individuals

We numerical simulate the propagation behaviour and people distribution trait of epidemic spreading in mobile individuals by using cellular automaton method. The simulation results show that there exists a critical value of infected rate fluctuating amplitude, above which the epidemic can spread in whole population. Moreover, with the value of infected rate fluctuating amplitude increasing, the spatial distribution of infected population exhibits the spontaneous formation of irregular spiral waves and convergence phenomena, at the same time, the density of different populations will oscillate automatically with time. What is more, the traits of dynamic grow clearly and stably when the time and the value of infected rate fluctuating amplitude increasing. It is also found that the maximal proportion of infected individuals is independent of the value of fluctuating amplitude rate, but increases linearly with the population density.

Steady States in SIRS Epidemical Model of Mobile Individuals

We consider an epidemical model within socially interacting mobile individuals to study the behaviors of steady states of epidemic propagation in 2D networks. Using mean-field approximation and large scale simulations, we recover the usual epidemic behavior with critical thresholds δc and pc below which infectious disease dies out. For the population density δ far above δc, it is found that there is linear relationship between contact rate λ and the population density δ in the main. At the same time, the result obtained from mean-field approximation is compared with our numerical result, and it is found that these two results are similar by and large but not completely the same.

Investigation of Dynamic Behavior in 1D Non-uniform Granular System

We present a non-uniform granular system in one-dimensional case, whose granularity distribution has the fractal characteristic. The particles are subject to inelastic mutual collisions and obey Langevin equation between collisions. By Monte Carlo simulation we study the dynamic actions of the system. Far from the equilibrium, i.e., τ τc , the results of simulation indicate that the inhomogeneity of the system and the inelasticity of the particles have great influences on the dynamic properties of the system, and correspondingly the influence of the inhomogeneity is more significant.

Moment Analysis of a Rice-Pile Model

Large scale simulations of a rice-pile model are performed. We use moment analysis techniques to evaluate critical exponents and data collapse method to verify the obtained results. The moment analysis yields well-defined avalanche exponents, which show that the rice-pile model can be coherently described within a finite size scaling framework. The general picture resulting from our analysis allows us to characterize the large scale behavior of the present model with great accuracy.

Criticality of Parasitic Disease Transmission in a Diffusive Population

Through using the methods of finite-size effect and short time dynamic scaling, we study the critical behavior of parasitic disease spreading process in a diffusive population mediated by a static vector environment. Through comprehensive analysis of parasitic disease spreading we find that this model presents a dynamical phase transition from disease-free state to endemic state with a finite population density. We determine the critical population density, above which the system reaches an epidemic spreading stationary state. We also perform a scaling analysis to determine the order parameter and critical relaxation exponents. The results show that the model does not belong to the usual directed percolation universality class and is compatible with the class of directed percolation with diffusive and conserved fields.

Sandpile Dynamics Driven by Degree on Scale-Free Networks

We introduce a sandpile model driven by degree on scale-free networks, where the perturbation is triggered at nodes with the same degree. We numerically investigate the avalanche behaviour of sandpile driven by different degrees on scale-free networks. It is observed that the avalanche area has the same behaviour with avalanche size. When the sandpile is driven at nodes with the minimal degree, the avalanches of our model behave similarly to those of the original Bak–Tang–Wiesenfeld (BTW) model on scale-free networks. As the degree of driven nodes increases from the minimal value to the maximal value, the avalanche distribution gradually changes from a clean power law, then a mixture of Poissonian and power laws, finally to a Poisson-like distribution. The average avalanche area is found to increase with the degree of driven nodes so that perturbation triggered on higher-degree nodes will result in broader spreading of avalanche propagation.

Influence of Inhomogeneity on Critical Behavior of Earthquake Model on Random Graph

We consider the earthquake model on a random graph. A detailed analysis of the probability distribution of the size of the avalanches will be given. The model with different inhomogeneities is studied in order to compare the critical behavior of different systems. The results indicate that with the increase of the inhomogeneities, the avalanche exponents reduce, i.e., the different numbers of defects cause different critical behaviors of the system. This is virtually ascribed to the dynamical perturbation.

Self-organized Criticality in an Earthquake Model on Random Network

A simplified Olami–Feder–Christensen model on a random network has been studied. We propose a new toppling rule — when there is an unstable site toppling, the energy of the site is redistributed to its nearest neighbors randomly not averagely. The simulation results indicate that the model displays self-organized criticality when the system is conservative, and the avalanche size probability distribution of the system obeys finite size scaling. When the system is nonconservative, the model does not display scaling behavior. Simulation results of our model with different nearest neighbors q is also compared, which indicates that the spatial topology does not alter the critical behavior of the system.