Hui-Jie Yang

Efficient routing on scale-free networks based on local information

In this Letter, we propose a new routing strategy with a single tunable parameter α only based on local information of network topology. The probability that a given node i with degree ki receives packets from its neighbors is proportional to k α . 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. Through 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 whi ch does not depend on α. This phenomenon suggests that the capacity of the scale-free network can be enhanced by increasing the forwarding ability of small important nodes which bear severe congestion.  2005 Elsevier B.V. All rights reserved.

Relations between average distance, heterogeneity and network synchronizability

By using the random interchanging algorithm, we investigate the relations between average distance, standard deviation of degree distribution and synchronizability of complex networks. We find that both increasing the average distance and magnifying the degree deviation will make the network synchronize harder. Only the combination of short average distance and small standard deviation of degree distribution ensures strong synchronizability. Some previous studies assert that the maximal betweenness is the right quantity to estimate network synchronizability: the larger the maximal betweenness, the poorer the network synchronizability. Here we address an interesting case, which strongly suggests that the single quantity, maximal betweenness, may not give a comprehensive description of network synchronizability.

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, StandardP with the scale-invariance exponents all in the interval [0.92,0.95]. We also estimate the local scaling exponents which indicate the financial time series is homogenous perfectly. In addition, a parsimonious percolation model for stock markets is proposed, of which the scaling behavior agrees with the real-life markets well.

Scale invariance of human electroencephalogram signals in sleep

In this paper, we investigate the dynamical properties of electroencephalogram (EEG) signals of humans in sleep. By using a modified random walk method, we demonstrate that scale-invariance is embedded in EEG signals after a detrending procedure is applied. Furthermore, we study the dynamical evolution of the probability density function (PDF) of the detrended EEG signals by nonextensive statistical modeling. It displays a scale-independent property, which is markedly different from the usual scale-dependent PDF evolution and cannot be described by the Fokker-Planck equation.

Collective chaos induced by structures of complex networks

Mapping a complex network of N coupled identical oscillators to a quantum system, the nearest neighbor level spacing (NNLS) distribution is used to identify collective chaos in the corresponding classical dynamics on the complex network. The classical dynamics on an Erdos–Renyi network with the wiring probability pER⩽1/N is in the state of collective order, while that on an Erdos–Renyi network with pER>1/N in the state of collective chaos. The dynamics on a WS Small-world complex network evolves from collective order to collective chaos rapidly in the region of the rewiring probability pr∈[0.0,0.1], and then keeps chaotic up to pr=1.0. The dynamics on a Growing Random Network (GRN) is in a special state deviates from order significantly in a way opposite to that on WS small-world networks. Each network can be measured by a couple values of two parameters (β,η).

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

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 simultaneously from the present self-organized model of coevolutionary processes.

Diffusion entropy analysis on the stride interval fluctuation of human gait

In this paper, the diffusion entropy technique is applied to investigate the scaling behavior of stride interval fluctuations of human gait. The scaling behaviors of the stride interval of human walking at norm, slow, and fast rate are similar; with the scale-invariance exponents in the interval [0.663,0.955], of which the mean value is 0.821±0.011. Dynamical analysis of these stride interval fluctuations reveals a self-similar pattern: fluctuation at one time scale are statistically similar to those at multiple other time scales, at least over hundreds of steps, while the healthy subjects walk at their norm rate. The long-range correlations are observed during the spontaneous walking by removal of the trend in the time series with Fourier filter. These findings uncover that the fractal dynamics of stride interval fluctuation of human gait are normally intrinsic to the locomotor systems.

Synchronizabilities of networks: A new index

The random matrix theory is used to bridge the network structures and the dynamical processes defined on them. We propose a possible dynamical mechanism for the enhancement effect of network structures on synchronization processes, based upon which a dynamic-based index of the synchronizability is introduced in the present paper.