Wang Bing-Hong

Epidemic spread in weighted scale-free networks

We investigate the detailed epidemic spreading process in scale-free networks with link weights that denote familiarity between two individuals. It is found that the spreading velocity reaches a peak quickly then decays in a power-law form. Numerical study exhibits that the nodes with larger strength is preferential to be infected, but the hierarchical dynamics are not clearly found, which is different from the well-known result in the unweighed network case. In addition, also by numerical study, we demonstrate that larger dispersion of weight of networks results in slower spreading, which indicates that epidemic spreads more quickly on unweighted scale-free networks than on weighted scale-free networks with the same condition.

Epidemic dynamics on complex networks

Abstract Recently, motivated by the pioneer work in revealing the small-world effect and scale-free property of various real-life networks, many scientists devote themselves to studying complex networks. One of the ultimate goals is to understand how the topological structures affect the dynamics upon networks. In this paper, we give a brief review on the studies of epidemic dynamics on complex networks, including by description of classical epidemic models, the epidemic spread on small-world and scale-free networks, and network immunization. Finally, perspectives and some interesting problems are proposed. * Supported National Natural Science Foundation of China (Grant Nos. 70471033, 10472116, and 70271070), and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20020358009)

Self-organized criticality of forest fire in China

Self-organized criticality (SOC) of forest fire is studied from an analysis of a large series of forest-fire records from 1950 to 1989 in China. The time-invariant, scale-invariant characteristics of SOC of forest fire in China are analyzed in detail. The deviations between the occurrence frequency of very large fires and the power-law relation are explained by the forest-fire model with tree immunity (FFMTI). Actual forest-fire records are compared with the simulation results of a self-organized critical forest-fire model. It is shown that the forest-fire model applies well to explain the SOC characteristics of a forest fire. SOC characteristics have practical implications on forest-fire protection.

Heavy-Tailed Statistics in Short-Message Communication

Short-message (SM) is one of the most frequently used communication channels in the modern society. In this Brief Report, based on the SM communication records provided by some volunteers, we investigate the statistics of SM communication pattern, including the interevent time distributions between two consecutive short messages and two conversations, and the distribution of message number contained by a complete conversation. In the individual level, the current empirical data raises a strong evidence that the human activity pattern, exhibiting a heavy-tailed interevent time distribution, is driven by a non-Poisson nature.Short-message (SM) is one of the most frequently used communication channels in modern society. Based on the SM communication records provided by some volunteers, we investigate the statistics of SM communication pattern, including the inter-event time distributions between two consecutive short messages and two conversations, and the distribution of message number contained in a complete conversation. In the individual level, the empirical result raises strong evidence that the human activity pattern, exhibiting a heavy-tailed inter-event time distribution, is driven by a non-Poisson process.

Interest-driven model for human dynamics

Empirical observations indicate that the interevent time distribution of human actions exhibits heavy-tailed features. The queuing model based on task priorities is to some extent successful in explaining the origin of such heavy tails, however, it cannot explain all the temporal statistics of human behavior especially for the daily entertainments. We propose an interest-driven model, which can reproduce the power-law distribution of interevent time. The exponent can be analytically obtained and is in good accordance with the simulations. This model well explains the observed relationship between activities and power-law exponents, as reported recently for web-based behavior and the instant message communications.

Network Entropy Based on Topology Configuration and Its Computation to Random Networks

A definition of network entropy is presented, and as an example, the relationship between the value of network entropy of ER network model and the connect probability p as well as the total nodes N is discussed. The theoretical result and the simulation result based on the network entropy of the ER network are in agreement well with each other. The result indicated that different from the other network entropy reported before, the network entropy defined here has an obvious difference from different type of random networks or networks having different total nodes. Thus, this network entropy may portray the characters of complex networks better. It is also pointed out that, with the aid of network entropy defined, the concept of equilibrium networks and the concept of non-equilibrium networks may be introduced, and a quantitative measurement to describe the deviation to equilibrium state of a complex network is carried out.

An Efficient Control Strategy of Epidemic Spreading on Scale-Free Networks

We present a novel and effective method for controlling epidemic spreading on complex networks, especially on scale-free networks. The proposed strategy is performed by deleting edges according to their significances (the significance of an edge is defined as the product of the degrees of two nodes of this edge). In contrast to other methods, e.g., random immunization, proportional immunization, targeted immunization, acquaintance immunization and so on, which mainly focus on how to delete nodes to realize the control of epidemic spreading on complex networks, our method is more effective in realizing the control of epidemic spreading on complex networks, moreover, such a method can better retain the integrity of complex networks.

An approach to Hang Seng Index in Hong Kong stock market based on network topological statistics

Using homogenous partition of coarse graining process, the time series of Hang Seng Index (HSI) in Hong Kong stock market is transformed into discrete symbolic sequences S={S1S2S3...}, Si ∈ (R, r, d, D). Weighted networks of stock market are constructed by vertices that are 16 2-symbol strings (i.e. 16 patterns of HSI variations), and encode stock market relevant information about interconnections and interactions between fluctuation patterns of HSI in networks topology. By means of the measurements of betweenness centrality (BC) in networks, we have at least obtained 3 highest betweenneess centrality uniform vertices in 2 order of magnitude of time subinterval scale, i.e. 18.7% vertices undertake 71.9% betweenness centrality of networks, showing statistical stability. These properties cannot be found in random networks; here vertices almost have identical betweenness centrality. By comparison to random networks, we conclude that Hong Kong stock market, rather than a random system, is statistically stable.

Notes on the Algorithm for Calculating Betweenness

We investigate a common used algorithm [Phys. Rev. E 64 (2001) 016132] to calculate the betweenness centrality for all vertices. The inaccurateness of that algorithm is pointed out and a corrected algorithm, also with O(MN) time complexity, is given. In addition, the comparison of calculating results for these two algorithm aiming at the protein interaction network of yeast is shown.In this comment, we investigate a common used algorithm proposed by Newman [M. E. J. Newman, Phys. Rev. E {\bf 64}, 016132(2001)] to calculate the betweenness centrality for all vertices. The inaccurateness of Newmans algorithm is pointed out and a corrected algorithm, also with O(

Synchronizability of Highly Clustered Scale-Free Networks

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