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Bing-Hong Wang

Publications and source records attributed to Bing-Hong Wang.

At least 91 records · Page 5Linked to original sources
arXiv2008

An effective local routing strategy on the BA network

In this paper, We propose a effective routing strategy on the basis of the so-called nearest neighbor search strategy by introducing a preferential delivering exponent alpha. we assume that the handling capacity of one vertex is proportional to its degree when the degree is smaller than a cut-off value $K$, and is infinite otherwise. It is found that by tuning the parameter alpha, the scale-free network capacity measured by the order parameter is considerably enhanced compared to the normal nearest-neighbor strategy. Traffic dynamics both near and far away from the critical generating rate R_c are discussed. We also investigate R_c as functions of m (connectivity density), K (cutoff value). Due to the low cost of acquiring nearest-neighbor information and the strongly improved network capacity, our strategy may be useful and reasonable for the protocol designing of modern communication networks.

physics.data-an↗
arXiv2008

Parameter-tuning Networks: Experiments and Active Walk Model

The tuning process of a large apparatus of many components could be represented and quantified by constructing parameter-tuning networks. The experimental tuning of the ion source of the neutral beam injector of HT-7 Tokamak is presented as an example. Stretched-exponential cumulative degree distributions are found in the parameter-tuning networks. An active walk model with eight walkers is constructed. Each active walker is a particle moving with friction in an energy landscape; the landscape is modified by the collective action of all the walkers. Numerical simulations show that the parameter-tuning networks generated by the model also give stretched exponential functions, in good agreement with experiments. Our methods provide a new way and a new insight to understand the action of humans in the parameter-tuning of experimental processes, is helpful for experimental research and other optimization problems.

physics.plasm-ph↗
arXiv2008

Role of Activity in Human Dynamics

The human society is a very complex system; still, there are several non-trivial, general features. One type of them is the presence of power-law distributed quantities in temporal statistics. In this Letter, we focus on the origin of power-laws in rating of movies. We present a systematic empirical exploration of the time between two consecutive ratings of movies (the interevent time). At an aggregate level, we find a monotonous relation between the activity of individuals and the power-law exponent of the interevent-time distribution. At an individual level, we observe a heavy-tailed distribution for each user, as well as a negative correlation between the activity and the width of the distribution. We support these findings by a similar data set from mobile phone text-message communication. Our results demonstrate a significant role of the activity of individuals on the society-level patterns of human behavior. We believe this is a common character in the interest-driven human dynamics, corresponding to (but different from) the universality classes of task-driven dynamics.

physics.soc-ph↗
arXiv2008

Opinion dynamics on directed small-world networks

In this paper, we investigate the self-affirmation effect on formation of public opinion in a directed small-world social network. The system presents a non-equilibrium phase transition from a consensus state to a disordered state with coexistence of opinions. The dynamical behaviors are very sensitive to the density of long-range interactions and the strength of self-affirmation. When the long-range interactions are sparse and individual generally does not insist on his/her opinion, the system will display a continuous phase transition, in the opposite case with high self-affirmation strength and dense long-range interactions, the system does not display a phase transition. Between those two extreme cases, the system undergoes a discontinuous phase transition.

physics.soc-ph↗
arXiv2007

Synchronization on complex networks with different sorts of communities

In this paper, inspired by the idea that many real networks are composed by different sorts of communities, we investigate the synchronization property of oscillators on such networks. We identify the communities by the intrinsic frequencies probability density $g(ω)$ of Kuramoto oscillators. That is to say, communities in different sorts are functional different. For a network containing two sorts of communities, when the community strength is strong, only the oscillators in the same community synchronize. With the weakening of the community strength, an interesting phenomenon, \emph{Community Grouping}, appears: although the global synchronization is not achieved, oscillators in the same sort of communities will synchronize. Global synchronization will appear with the further reducing of the community strength, and the oscillators will rotate around the average frequency.

physics.data-an↗
arXiv2007

The process of coevolutionary competitive exclusion: speciation, multifractality and power-laws in correlation

Competitive exclusion, a key principle of ecology, can be generalized to understand many other complex systems. Individuals under surviving pressure tend to be different from others, and correlations among them change correspondingly to the updating of their states. We show with numerical simulation that these aptitudes can contribute to group formation or speciation in social fields. Moreover, they can lead to power-law topological correlations of complex networks. By coupling updating states of nodes with variation of connections in a network, structural properties with power-laws and functions like multifractality, spontaneous ranking and evolutionary branching of node states can emerge out simultaneously from the present self-organized model of coevolutionary process.

physics.data-an↗
arXiv2007

A limited resource model of fault-tolerant capability against cascading failure of complex network

We propose a novel capacity model for complex networks against cascading failure. In this model, vertices with both higher loads and larger degrees should be paid more extra capacities, i.e. the allocation of extra capacity on vertex $i$ will be proportional to $ k_{i}^γ $, where $ k_{i}$ is the degree of vertex $i$ and $γ>0$ is a free parameter. We have applied this model on Barabási-Albert network as well as two real transportation networks, and found that under the same amount of available resource, this model can achieve better network robustness than previous models.

physics.soc-ph↗
arXiv2007

Enhance synchronizability via age-based coupling

In this brief report, we study the synchronization of growing scale-free networks. An asymmetrical age-based coupling method is proposed with only one free parameter $α$. Although the coupling matrix is asymmetric, our coupling method could guarantee that all the eigenvalues are non-negative reals. The eigneratio R will approach to 1 in the large limit of $α$.

cond-mat.dis-nn↗
arXiv2007

Empirical study on clique-degree distribution of networks

The community structure and motif-modular-network hierarchy are of great importance for understanding the relationship between structures and functions. In this paper, we investigate the distribution of clique-degree, which is an extension of degree and can be used to measure the density of cliques in networks. The empirical studies indicate the extensive existence of power-law clique-degree distributions in various real networks, and the power-law exponent decreases with the increasing of clique size.

physics.soc-ph↗
arXiv2007

Epidemic spreading and immunization with identical infectivity

In this paper, a susceptible-infected-susceptible (SIS) model with identical infectivity, where each node is assigned with the same capability of active contacts, $A$, at each time step, is presented. We found that on scale-free networks, the density of the infected nodes shows the existence of threshold, whose value equals 1/A, both demonstrated by analysis and numerical simulation. The infected population grows in an exponential form and follows hierarchical dynamics, indicating that once the highly connected hubs are reached, the infection pervades almost the whole network in a progressive cascade. In addition, the effects of random, proportional, and targeted immunization for this model are investigated. Based on the current model and for heterogenous networks, the targeted strategy performs best, while the random strategy is much more efficient than in the standard SIS model. The present results could be of practical importance in the setup of dynamic control strategies.

physics.soc-ph↗
arXiv2007

Scale-invariance of human EEG signals in sleep

We investigate the dynamical properties of electroencephalogram (EEG) signals of human in sleep. By using a modified random walk method, We demonstrate that the scale-invariance is embedded in EEG signals after a detrending procedure. Further more, we study the dynamical evolution of probability density function (PDF) of the detrended EEG signals by nonextensive statistical modeling. It displays scale-independent property, which is markedly different from the turbulent-like scale-dependent PDF evolution.

physics.bio-ph↗
arXiv2007

Traffic dynamics of packets generated with non-homogeneously selected sources and destinations in scale-free networks

In this paper, we study traffic dynamics in scale-free networks in which packets are generated with non-homogeneously selected sources and destinations, and forwarded based on the local routing strategy. We consider two situations of packet generation: (i) packets are more likely generated at high-degree nodes; (ii) packets are more likely generated at low-degree nodes. Similarly, we consider two situations of packet destination: (a) packets are more likely to go to high-degree nodes; (b) packets are more likely to go to low-degree nodes. Our simulations show that the network capacity and the optimal value of $α$ corresponding to the maximum network capacity greatly depend on the configuration of packets' sources and destinations. In particular, the capacity is greatly enhanced when most packets travel from low-degree nodes to high-degree nodes.

physics.soc-ph↗
arXiv2006

Geographical networks evolving with optimal policy

In this article, we propose a growing network model based on an optimal policy involving both topological and geographical measures. In this model, at each time step, a new node, having randomly assigned coordinates in a $1 \times 1$ square, is added and connected to a previously existing node $i$, which minimizes the quantity $r_i^2/k_i^α$, where $r_i$ is the geographical distance, $k_i$ the degree, and $α$ a free parameter. The degree distribution obeys a power-law form when $α=1$, and an exponential form when $α=0$. When $α$ is in the interval $(0,1)$, the network exhibits a stretched exponential distribution. We prove that the average topological distance increases in a logarithmic scale of the network size, indicating the existence of the small-world property. Furthermore, we obtain the geographical edge-length distribution, the total geographical length of all edges, and the average geographical distance of the whole network. Interestingly, we found that the total edge-length will sharply increase when $α$ exceeds the critical value $α_c=1$, and the average geographical distance has an upper bound independent of the network size. All the results are obtained analytically with some reasonable approximations, which are well verified by simulations.

physics.soc-ph↗
arXiv2006

Memory-Based Snowdrift Game on Networks

We present a memory-based snowdrift game (MBSG) taking place on networks. We found that, when a lattice is taken to be the underlying structure, the transition of spatial patterns at some critical values of the payoff parameter is observable for both 4 and 8-neighbor lattices. The transition points as well as the styles of spatial patterns can be explained by local stability analysis. In sharp contrast to previously reported results, cooperation is promoted by the spatial structure in the MBSG. Interestingly, we found that the frequency of cooperation of the MBSG on a scale-free network peaks at a specific value of the payoff parameter. This phenomenon indicates that properly encouraging selfish behaviors can optimally enhance the cooperation. The memory effects of individuals are discussed in detail and some non-monotonous phenomena are observed on both lattices and scale-free networks. Our work may shed some new light on the study of evolutionary games over networks.

physics.soc-ph↗
arXiv2006

Immunization of Susceptible-Infected Model on Scale-Free networks

In this paper, we investigate two major immunization strategies, random immunization and targeted immunization, of the susceptible-infected (SI) Model on the Barabási-Albert (BA) networks. For the heterogenous structure, the random strategy is quite ineffective if the vaccinated proportion is quite small, while the targeted one which prefers to vaccinate the individuals with the largest degree can sharply depress the epidemic spreading even only a small amount of population are vaccinated. The analytical solution is also obtained,which can capture the trend of velocity change versus the amount of vaccinated population.

physics.bio-ph↗
arXiv2006

Power-law Strength-Degree Correlation From a Resource-Allocation Dynamics on Weighted Networks

Many weighted scale-free networks are known to have a power-law correlation between strength and degree of nodes, which, however, has not been well explicated. We investigate the dynamic behaviors of resource/traffic flow on scale-free networks. The dynamical system will evolve to a kinetic equilibrium state, where the strength, defined by the amount of resource or traffic load, is correlated with the degree in a power-law form with tunable exponent. The analytical results agree with simulations well.

physics.soc-ph↗
arXiv2006

Naming Game on small-world networks: the role of clustering structure

Naming Game is a recently proposed model for describing how a multi-agent system can converge towards a consensus state in a self-organized way. In this paper, we investigate this model on the so-called homogeneous small-world networks and focus on the influence of the triangular topology on the dynamics. Of all the topological quantities, the clustering coefficient is found to play a significant role in the dynamics of the Naming Game. On the one hand, it affects the maximum memory of each agent; on the other hand, it inhibits the growing of clusters in which agents share a common word, i.e., a larger clustering coefficient will cause a slower convergence of the system. We also find a quantitative relationship between clustering coefficient and the maximum memory.

physics.data-an↗