arXiv subjects

Bing-Hong Wang

Publications and source records attributed to Bing-Hong Wang.

At least 127 records · Page 7Linked to original sources

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

Better Synchronizability Predicted by Crossed Double Cycle

In this brief report, we propose a network model named crossed double cycles, which are completely symmetrical and can be considered as the extensions of nearest-neighboring lattices. The synchronizability, measured by eigenratio $R$, can be sharply enhanced by adjusting the only parameter, crossed length $m$. The eigenratio $R$ is shown very sensitive to the average distance $L$, and the smaller average distance will lead to better synchronizability. Furthermore, we find that, in a wide interval, the eigenratio $R$ approximately obeys a power-law form as $R\sim L^{1.5}$.

cond-mat.stat-mech↗

arXiv2005

Scale-free networks without growth

In this letter, we proposed an ungrowing scale-free network model, wherein the total number of nodes is fixed and the evolution of network structure is driven by a rewiring process only. In spite of the idiographic form of $G$, by using a two-order master equation, we obtain the analytic solution of degree distribution in stable state of the network evolution under the condition that the selection probability $G$ in rewiring process only depends on nodes' degrees. A particular kind of the present networks with $G$ linearly correlated with degree is studied in detail. The analysis and simulations show that the degree distributions of these networks can varying from the Possion form to the power-law form with the decrease of a free parameter $α$, indicating the growth may not be a necessary condition of the self-organizaton of a network in a scale-free structure.

cond-mat.stat-mech↗

arXiv2005

Comment on ``Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality"

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 Newman's 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 the protein interaction network of Yeast is shown.

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

Growing Directed Networks: Organization and Dynamics

We study the organization and dynamics of growing directed networks. These networks are built by adding nodes successively in such a way that each new node has $K$ directed links to the existing ones. The organization of a growing directed network is analyzed in terms of the number of ``descendants'' of each node in the network. We show that the distribution $P(S)$ of the size, $S$, of the descendant cluster is described generically by a power-law, $P(S) \sim S^{-η}$, where the exponent $η$ depends on the value of $K$ as well as the strength of preferential attachment. We determine that, in the case of growing random directed networks without any preferential attachment, $η$ is given by $1+1/K$. We also show that the Boolean dynamics of these networks is stable for any value of $K$. However, with a small fraction of reversal in the direction of the links, the dynamics of growing directed networks appears to operate on ``the edge of chaos'' with a power-law distribution of the cycle lengths. We suggest that the growing directed network may serve as another paradigm for the emergence of the scale-free features in network organization and dynamics.

cond-mat.stat-mech↗

arXiv2005

Efficient routing on complex networks

In this letter, we propose a new routing strategy to improve the transportation efficiency on complex networks. Instead of using the routing strategy for shortest path, we give a generalized routing algorithm to find the so-called {\it efficient path}, which considers the possible congestion in the nodes along actual paths. Since the nodes with largest degree are very susceptible to traffic congestion, an effective way to improve traffic and control congestion, as our new strategy, can be as redistributing traffic load in central nodes to other non-central nodes. Simulation results indicate that the network capability in processing traffic is improved more than 10 times by optimizing the efficient path, which is in good agreement with the analysis.

cond-mat.dis-nn↗

arXiv2005

Diffusion entropy analysis on the scaling behavior of financial markets

In this paper the diffusion entropy technique is applied to investigate the scaling behavior of financial markets. The scaling behaviors of four representative stock markets, Dow Jones Industrial Average, Standard&Poor 500, Heng Seng Index, and Shang Hai Stock Synthetic Index, are almost the same; with the scale-invariance exponents all in the interval $[0.92, 0.95]$. These results provide a strong evidence of the existence of long-rang correlation in financial time series, thus several variance-based methods are restricted for detecting the scale-invariance properties of financial markets. In addition, a parsimonious percolation model for stock markets is proposed, of which the scaling behavior agrees with the real-life markets well.

physics.soc-ph↗

arXiv2005

Epidemic dynamics on complex networks

Recently, motivated by the pioneer works that reveal the small-world effect and scale-free property of various real-life networks, many scientists devote themselves into studying complex networks. One of the ultimate goals is to understand how the topological structures of networks affect the dynamics upon them. In this paper, we give a brief review on the studies of epidemic dynamics on complex networks, including the description of classical epidemic models, the epidemic spread on small-world and scale-free networks, and network immunization. Finally, a prospect is addressed and some interesting problems are listed.

physics.soc-ph↗

arXiv2005

Self-organized Boolean game on networks

A model of Boolean game with only one free parameter $p$ that denotes the strength of herd behavior is proposed where each agent acts according to the information obtained from his neighbors in network and those in the minority are rewarded. The simulation results indicate that the dynamic of system is sensitive to network topology, where the network of larger degree variance, i.e. the system of greater information heterogeneity, leads to less system profit. The system can self-organize to a stable state and perform better than random choice game, although only the local information is available to the agents. In addition, in heterogeneity networks, the agents with more information gain more than those with less information for a wide extent of herd strength $p$.

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

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 general model for collaboration networks

In this paper, we propose a general model for collaboration networks. Depending on a single free parameter "{\bf preferential exponent}", this model interpolates between networks with a scale-free and an exponential degree distribution. The degree distribution in the present networks can be roughly classified into four patterns, all of which are observed in empirical data. And this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, we find a peak distribution of act-size from empirical data which has not been emphasized before of some collaboration networks. Our model can produce the peak act-size distribution naturally that agrees with the empirical data well.

cond-mat.stat-mech↗

arXiv2005

Simplex triangulation induced scale-free networks

We propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called simplex triangulation networks(STNs) as they can be considered as a kind of network representation of simplex triangulation. We obtain the analytic results of power-law exponent $γ=2+\frac{1}{d-1}$ for $d$-dimensional STNs, and clustering coefficient $C$. We prove that the increasing tendency of average distance of STNs is a little slower than the logarithm of the number of nodes in STNs. In addition, the STNs possess hierarchical structure as $C(k)\sim k^{-1}$ when $k\gg d$ that in accord with the observations of many real-life networks.

cond-mat.stat-mech↗

arXiv2005

Integer Networks

Inspired by Pythagoras's belief that numbers are the absolute reality, we obtain some demonstrational results about topological properties of integer networks, in which the vertices represent integers and two vertices are neighbors if and only if there exists a divisibility relation between them. We strictly prove that the diameter of networks has a constant upper bound independent to the network size $N$, which is completely different from the extensively studied real-life networks with their average distance increasing logarithmically to $N$ as $L\sim \texttt{ln}N$ or $L\sim \texttt{lnln}N$. Further more, the integer networks is high clustered, with clustered coefficient $C\approx 0.34$, and display power-law degree distribution of exponent $γ\approx 2.4$.

cond-mat.stat-mech↗

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↗