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Wen-Xu Wang

Publications and source records attributed to Wen-Xu Wang.

54 records · Page 3Linked to original sources
arXiv2006

Interplay between evolutionary game and network structure: the coevolution of social net, cooperation and wealth

We study the interplay between evolutionary game and network structure and show how the dynamics of the game affect the growth pattern of the network and how the evolution of the network influence the cooperative behavior in the game. Simulation results show that the payoff-based preferential attachment mechanism leads to the emergence of a scale-free structural property, $P(k)\sim k^{-γ}$. Moreover, we investigate the average path length and the assortative mixing features. The obtained results indicate that the network has small-world and positive assortative behaviors, which are consistent with the observations of some real social networks. In parallel, we found that the evolution of the underlying network structure effectively promotes the cooperation level of the game. We also investigate the wealth distribution obtained by our model, which is consistent with the Pareto law in the real observation. In addition, the analysis of the generated scale-free network structure is provided for better understanding the evolutionary dynamics of our model.

physics.soc-ph↗
arXiv2006

Epidemic spreading on heterogeneous networks with identical infectivity

In this paper, we propose a modified susceptible-infected-recovered (SIR) model, in which each node is assigned with an identical capability of active contacts, $A$, at each time step. In contrast to the previous studies, we find that on scale-free networks, the density of the recovered individuals in the present model shows a threshold behavior. We obtain the analytical results using the mean-field theory and find that the threshold value equals 1/A, indicating that the threshold value is independent of the topology of the underlying network. The simulations agree well with the analytic results. Furthermore, we study the time behavior of the epidemic propagation and find a hierarchical dynamics with three plateaus. Once the highly connected hubs are reached, the infection pervades almost the whole network in a progressive cascade across smaller degree classes. Then, after the previously infected hubs are recovered, the disease can only propagate to the class of smallest degree till the infected individuals are all recovered. The present results could be of practical importance in the setup of dynamic control strategies.

physics.soc-ph↗
arXiv2006

Geographical effects on epidemic spreading in scale-free networks

Many real networks are embedded in a metric space: the interactions among individuals depend on their spatial distances and usually take place among their nearest neighbors. In this paper, we introduce a modified susceptible-infected-susceptible (SIS) model to study geographical effects on the spread of diseases by assuming that the probability of a healthy individual infected by an infectious one is inversely proportional to the Euclidean distance between them. It is found that geography plays a more important role than hubs in disease spreading: the more geographically constrained the network is, the more highly the epidemic prevails.

physics.soc-ph↗
arXiv2006

Urban Traffic Dynamics: A Scale-Free Network Perspective

This letter propose a new model for characterizing traffic dynamics in scale-free networks. With a replotted road map of cities with roads mapped to vertices and intersections to edges, and introducing the road capacity L and its handling ability at intersections C, the model can be applied to urban traffic system. Simulations give the overall capacity of the traffic system which is quantified by a phase transition from free flow to congestion. Moreover, we report the fundamental diagram of flow against density, in which hysteresis is found, indicating that the system is bistable in a certain range of vehicle density. In addition, the fundamental diagram is significantly different from single-lane traffic model and 2-D BML model with four states: free flow, saturated flow, bistable and jammed.

physics.soc-ph↗
arXiv2006

A Unified Framework for the Pareto Law and Matthew Effect using Scale-Free Networks

We investigate the accumulated wealth distribution by adopting evolutionary games taking place on scale-free networks. The system self-organizes to a critical Pareto distribution (1897) of wealth $P(m)\sim m^{-(v+1)}$ with $1.6 < v <2.0$ (which is in agreement with that of U.S. or Japan). Particularly, the agent's personal wealth is proportional to its number of contacts (connectivity), and this leads to the phenomenon that the rich gets richer and the poor gets relatively poorer, which is consistent with the Matthew Effect present in society, economy, science and so on. Though our model is simple, it provides a good representation of cooperation and profit accumulation behavior in economy, and it combines the network theory with econophysics.

physics.soc-ph↗
arXiv2006

Integrating static and dynamic information for routing traffic

The efficiency of traffic routing on complex networks can be reflected by two key measurements i.e. the system capacity and the average data packets travel time. In this paper, we propose a mixing routing strategy by integrating local static and dynamic information for enhancing the efficiency of traffic on scale-free networks. The strategy is governed by a single parameter. Simulation results show that there exists a optimal parameter value by considering both maximizing the network capacity and reducing the packet travel time. Comparing with the strategy by adopting exclusive local static information, the new strategy shows its advantages in improving the efficiency of the system. The detailed analysis of the mixing strategy is provided. This work suggests that how to effectively utilize the larger degree nodes plays the key role in the scale-free traffic systems.

physics.soc-ph↗
arXiv2006

Dynamical Coarse Graining of Large Scale-Free Boolean networks

We present a renormalization-grouplike method performed in the state space for detecting the dynamical behaviors of large scale-free Boolean networks, especially for the chaotic regime as well as the edge of chaos. Numerical simulations with different coarse-graining level show that the state space networks of scale-free Boolean networks follow universal power-law distributions of in and out strength, in and out degree, as well as weight. These interesting results indicate scale-free Boolean networks still possess self-organized mechanism near the edge of chaos in the chaotic regime. The number of state nodes as a function of biased parameter for distinct coarse-graining level also demonstrates that the power-law behaviors are not the artifact of coarse-graining procedure. Our work may also shed some light on the investigation of brain dynamics.

cond-mat.dis-nn↗
arXiv2006

Emergence of cooperation induced by preferential learning

The evolutionary Prisoner's Dilemma Game (PDG) and the Snowdrift Game (SG) with preferential learning mechanism are studied in the Barabási-Albert network. Simulation results demonstrate that the preferential learning of individuals remarkably promotes the cooperative behavior for both two games over a wide range of payoffs. To understand the effect of preferential learning on the evolution of the systems, we investigate the time series of the cooperator density for different preferential strength and payoffs. It is found that in some specific cases two games both show the $1/f$-scaling behaviors, which indicate the existence of long range correlation. We also figure out that when the large degree nodes have high probability to be selected, the PDG displays a punctuated equilibrium-type behavior. On the contrary, the SG exhibits a sudden increase feature. These temporary instable behaviors are ascribed to the strategy shift of the large degree nodes.

physics.soc-ph↗
arXiv2006

Diffusion-limited-aggregation on a directed small world network

For real world systems, nonuniform medium is ubiquitous. Therefore, we investigate the diffusion-limited-aggregation process on a two dimensional directed small-world network instead of regular lattice. The network structure is established by rewiring connections on the two dimensional directed lattice. Those rewired edges are controlled by two parameters $θ$ and $m$, which characterize the spatial length and the density of the long-range connections, respectively. Simulations show that there exists a maximum value of the fractal dimension when $θ$ equals zero. Interestingly, we find that the symmetry of the aggregation pattern is broken when rewired connections are long enough, which may be an explanation for the formation of asymmetrical fractal in nature. Then, we perform multifractal analysis on the patterns further.

physics.comp-ph↗
arXiv2005

Self-learning Mutual Selection Model for Weighted Networks

In this paper, we propose a self-learning mutual selection model to characterize weighted evolving networks. By introducing the self-learning probability $p$ and the general mutual selection mechanism, which is controlled by the parameter $m$, the model can reproduce scale-free distributions of degree, weight and strength, as found in many real systems. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$, depending on the parameters $m$ and $p$. The model can unify the characterization of both assortative and disassortative weighted networks. Also, we find that self-learning may contribute to the assortative mixing of social networks.

physics.soc-ph↗
arXiv2005

The synchronizability of highly clustered scale-free networks

In this letter, we consider the effect of clustering coefficient on the synchronizability of coupled oscillators located on scale-free networks. The analytic result for the value of clustering coefficient aiming at a highly clustered scale-free network model, the Holme-Kim is obtained, and the relationship between network synchronizability and clustering coefficient is reported. The simulation results strongly suggest that the more clustered the network, the poorer its synchronizability.

cond-mat.stat-mech↗
arXiv2005

Enhance synchronizability by structural perturbations

In this paper, we investigate the collective synchronization of system of coupled oscillators on Barabási-Albert scale-free network. We propose an approach of structural perturbations aiming at those nodes with maximal betweenness. This method can markedly enhance the network synchronizability, and is easy to be realized. The simulation results show that the eigenratio will sharply decrease to its half when only 0.6% of those hub nodes are under 3-division processes when network size N=2000. In addition, the present study also provides a theoretical evidence that the maximal betweenness plays a main role in network synchronization.

cond-mat.stat-mech↗
arXiv2005

Efficient routing on scale-free networks based on local information

In this letter, we propose a new routing strategy with a single free parameter $α$ only based on local information of network topology. In order to maximize the packets handling capacity of underlying structure that can be measured by the critical point of continuous phase transition from free flow to congestion, the optimal value of $α$ is sought out. By investigating the distributions of queue length on each node in free state, we give an explanation why the delivering capacity of the network can be enhanced by choosing the optimal $α$. Furthermore, dynamic properties right after the critical point are also studied. Interestingly, it is found that although the system enters the congestion state, it still possesses partial delivering capability which do not depend on $α$. This phenomenon suggests that the capacity of the network can be enhanced by increasing the forwarding ability of small important nodes which bear severe congestion.

physics.soc-ph↗
arXiv2005

Weighted Network Models Based on Local and Global Rules

We will introduce two evolving models that characterize weighted complex networks. Though the microscopic dynamics are different, these models are found to bear a similar mathematical framework, and hence exhibit some common behaviors, for example, the power-law distributions and evolution of degree, weight and strength. We also study the nontrivial clustering coefficient C and tunable degree assortativity coefficient r, depending on specific parameters. Most results are supported by present empirical evidences, and may provide us with a better description of the hierarchies and organizational architecture of weighted networks. Our models have been inspired by the weighted network model proposed by Alain Barrat et al. (BBV for short), and can be considered as a meaningful development of their original work.

cond-mat.dis-nn↗
arXiv2005

Generalized BBV Models for Weighted Complex Networks

We will introduce two evolving models that characterize weighted complex networks. Though the microscopic dynamics are different, these models are found to bear a similar mathematical framework, and hence exhibit some common behaviors, for example, the power-law distributions and evolution of degree, weight and strength. We also study the nontrivial clustering coefficient C and tunable degree assortativity coefficient r, depending on specific parameters. Most results are supported by present empirical evidences, and may provide us with a better description of the hierarchies and organizational architecture of weighted networks. Our models have been inspired by the weighted network model proposed by Alain Barrat et al. (BBV for short), and can be considered as a meaningful development of their original work.

nlin.AO↗
arXiv2005

A Mutual Attraction Model for Both Assortative and Disassortative Weighted Networks

In most networks, the connection between a pair of nodes is the result of their mutual affinity and attachment. In this letter, we will propose a Mutual Attraction Model to characterize weighted evolving networks. By introducing the initial attractiveness $A$ and the general mechanism of mutual attraction (controlled by parameter $m$), the model can naturally reproduce scale-free distributions of degree, weight and strength, as found in many real systems. Simulation results are in consistent with theoretical predictions. Interestingly, we also obtain nontrivial clustering coefficient C and tunable degree assortativity r, depending on $m$ and A. Our weighted model appears as the first one that unifies the characterization of both assortative and disassortative weighted networks.

cond-mat.dis-nn↗
arXiv2005

A Mutual Selection Model for Weighted Networks

For most networks, the connection between two nodes is the result of their mutual affinity and attachment. In this paper, we propose a mutual selection model to characterize the weighted networks. By introducing a general mechanism of mutual selection, the model can produce power-law distributions of degree, weight and strength, as confirmed in many real networks. Moreover, we also obtained the nontrivial clustering coefficient $C$, degree assortativity coefficient $r$ and degree-strength correlation, depending on a model parameter $m$. These results are supported by present empirical evidences. Studying the degree-dependent average clustering coefficient $C(k)$ and the degree-dependent average nearest neighbors' degree $k_{nn}(k)$ also provide us with a better description of the hierarchies and organizational architecture of weighted networks.

cond-mat.stat-mech↗
arXiv2005

General Dynamics of Topology and Traffic on Weighted Technological Networks

For most technical networks, the interplay of dynamics, traffic and topology is assumed crucial to their evolution. In this paper, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r and degree-strength correlation are all in consistence with empirical evidences.

cond-mat.dis-nn↗