arXiv subjects

Huijie Yang

Publications and source records attributed to Huijie Yang.

At least 19 recordsLinked to original sources

arXiv2018

Double transition of information spreading in a two-layered network

A great deal of significant progress has been seen in the study of information spreading on populations of networked individuals. A common point in many of past studies is that there is only one transition in the phase diagram of the final accepted size versus the transmission probability. However, whether other factors alter this phenomenology is still under debate, especially for the case of information spreading through many channels and platforms. In the present study, we adopt a two-layered network to represent the interactions of multiple channels and propose a SAR (Susceptible-Accepted-Recovered) information spreading model. Interestingly, our model shows a novel double transition including a continuous transition and a following discontinuous transition in the phase diagram, which originates from two outbreaks between the two layers of the network. Further, we reveal that the key factors are a weak coupling condition between the two layers, a large adoption threshold and the difference of the degree distributions between the two layers. Then, an edge-based compartmental theory is developed which fully explains all numerical results. Our findings may be of significance for understanding the secondary outbreaks of the information in real life.

physics.soc-ph↗

arXiv2018

Immediate Causality Network of Stock Markets

A financial system contains many elements networked by their relationships. Extensive works show that topological structure of the network stores rich information on evolutionary behaviors of the system such as early warning signals of collapses and/or crises. Existing works focus mainly on the network structure within a single stock market, while a collapse/crisis occurs in a macro-scale covering several or even all markets in the world. This mismatch of scale leads to unacceptable noise to the topological structure, and lack of information stored in relationships between different markets. In this work by using the transfer entropy we reconstruct the influential network between ten typical stock markets distributed in the world. Interesting findings include, before a financial crisis the connection strength reaches a maxima, which can act as an early warning signal of financial crises; The markets in America are mono-directionally and strongly influenced by that in Europe and act as the center; Some strongly linked pairs have also close correlations. The findings are helpful in understanding the evolution and modelling the dynamical process of the global financial system.

q-fin.ST↗

arXiv2018

Effective usage of random routing on networks of mobile agents

Most existing routing strategies to improve transport efficiency have little attention what order should the packets be delivered, just simply used first-in-first-out queue discipline. However, it is far from optimal. In this paper we apply priority queuing discipline to random routing strategy on networks of mobile agents, in which the packets have high priority to transfer directly to their destination despite their order in the queue if their destination are within a communication radius. Numerical experiments show that it not only remarkably improves network throughput and the packet arriving rate, but also reduces average travelling time and the rate of waiting time to travelling time. Our work may be helpful in routing strategy designing on networks of mobile agents.

cs.NI↗

arXiv2012

Hurst Exponents For Short Time Series

A new concept, called balanced estimator of diffusion entropy, is proposed to detect scalings in short time series. The effectiveness of the method is verified by means of a large number of artificial fractional Brownian motions. It is used also to detect scaling properties and structural breaks in stock price series of Shanghai Stock market.

q-fin.ST↗

arXiv2012

Evaluation of Scale-Invariance In Physiological Signals By Means Of Balanced Estimation Of Diffusion Entropy

By means of the concept of balanced estimation of diffusion entropy we evaluate reliable scale-invariance embedded in different sleep stages and stride records. Segments corresponding to Wake, light sleep, REM, and deep sleep stages are extracted from long-term EEG signals. For each stage the scaling value distributes in a considerable wide range, which tell us that the scaling behavior is subject- and sleep cycle- dependent. The average of the scaling exponent values for wake segments is almost the same with that for REM segments ($\sim 0.8$). Wake and REM stages have significant high value of average scaling exponent, compared with that for light sleep stages ($\sim 0.7$). For the stride series, the original diffusion entropy (DE) and balanced estimation of diffusion entropy (BEDE) give almost the same results for de-trended series. Evolutions of local scaling invariance show that the physiological states change abruptly, though in the experiments great efforts have been done to keep conditions unchanged. Global behaviors of a single physiological signal may lose rich information on physiological states. Methodologically, BEDE can evaluate with considerable precision scale-invariance in very short time series ($\sim 10^2$), while the original DE method sometimes may underestimate scale-invariance exponents or even fail in detecting scale-invariant behavior. The BEDE method is sensitive to trends in time series. Existence of trend may leads to a unreasonable high value of scaling exponent, and consequent mistake conclusions.

cond-mat.dis-nn↗

arXiv2011

Localizations on Complex Networks

We study the structural characteristics of complex networks using the representative eigenvectors of the adjacent matrix. The probability distribution function of the components of the representative eigenvectors are proposed to describe the localization on networks where the Euclidean distance is invalid. Several quantities are used to describe the localization properties of the representative states, such as the participation ratio, the structural entropy, and the probability distribution function of the nearest neighbor level spacings for spectra of complex networks. Whole-cell networks in the real world and the Watts-Strogatz small-world and Barabasi-Albert scale-free networks are considered. The networks have nontrivial localization properties due to the nontrivial topological structures. It is found that the ascending-order-ranked series of the occurrence probabilities at the nodes behave generally multifractally. This characteristic can be used as a structural measure of complex networks.

physics.soc-ph↗

arXiv2008

Self-affine Fractals Embedded in Spectra of Complex Networks

The scaling properties of spectra of real world complex networks are studied by using the wavelet transform. It is found that the spectra of networks are multifractal. According to the values of the long-range correlation exponent, the Hust exponent $H$, the networks can be classified into three types, namely, $H>0.5$, $H=0.5$ and $H<0.5$. All real world networks considered belong to the class of $H \ge 0.5$, which may be explained by the hierarchical properties.

physics.soc-ph↗

arXiv2007

Complexities of Human Promoter Sequences

By means of the diffusion entropy approach, we detect the scale-invariance characteristics embedded in the 4737 human promoter sequences. The exponent for the scale-invariance is in a wide range of $[ {0.3,0.9} ]$, which centered at $δ_c = 0.66$. The distribution of the exponent can be separated into left and right branches with respect to the maximum. The left and right branches are asymmetric and can be fitted exactly with Gaussian form with different widths, respectively.

q-bio.OT↗

arXiv2007

Synchronization reveals correlation between oscillators on networks

The understanding of synchronization ranging from natural to social systems has driven the interests of scientists from different disciplines. Here, we have investigated the synchronization dynamics of the Kuramoto dynamics departing from the fully synchronized regime. We have got the analytic expression of the dynamical correlation between pairs of oscillators that reveals the relation between the network dynamics and the underlying topology. Moreover, it also reveals the internal structure of networks that can be used as a new algorithm to detect community structures. Further, we have proposed a new measure about the synchronization in complex networks and scrutinize it in small-world and scale-free networks. Our results indicate that the more heterogeneous and "smaller" the network is, the more closely it would be synchronized by the collective dynamics.

cond-mat.dis-nn↗

arXiv2006

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.

cond-mat.stat-mech↗

arXiv2006

Nonlinear Modeling Approach to Human Promoter Sequences

By means of the nonlinear modeling technique (NM technique), we find the nonlinear deterministic structures in the promoter regions (PPRs) of DNA sequences, called deterministic valleys (DVs) in this paper. These DVs prefer to occur much more outside of a special region around TSS. The number, positions and shapes of the DVs are basically different for different PPRs. Generally, these DVs do not occur in the CpG islands, which tells us that they should be special structures with new biological functions rather than the CpG islands. PACS numbers: 87.14.Gg, 87.10.1e, 05.45.2a

q-bio.GN↗

arXiv2005

Scaling Invariance in Wave Functions of Quantum Systems on Complex Networks

Structure-induced features of the wave functions for the quantum systems on complex networks are discussed in this paper. For a quantum system on a network, the state corresponding to the eigenvalue close to the center of the spectrum is used as the representative state to display the impacts of the structure on the wave functions. We consider the Erdos-Renyi, the WS small world and the growing randomly network (GRN) models. It is found that the probability distribution functions (PDF) of the representative state's components can be described with a power-law with an exponential cutoff in a unified way. For Erdos-Renyi networks, with the increase of the connectivity probability $p_{ER} $ the PDF turns from power-law-dominated to exponential-dominated functions. For the WS networks in a special region of the rewiring probability $p_r \in (0,0.2)$, where this model can capture the features of real world networks, and the GRN networks, the PDFs obey almost a perfect power-law. These characteristics can be used as the structure measurements of complex networks. They can also provide useful information on dynamical processes on complex networks.

cond-mat.dis-nn↗

arXiv2005

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 $p_{ER} \le \frac{1}{N}$ is in the state of collective order, while that on an Erdos-Renyi network with $p_{ER} > \frac{1}{N}$ 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 $p_r \in [0.0,0.1]$, and then keeps chaotic up to $p_r = 1.0$. 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 $(β,η)$.

cond-mat.stat-mech↗

arXiv2005

Load Distribution on Small-world Networks

Mapping a complex network to an atomic cluster, the Anderson localization theory is used to obtain the load distribution on a complex network. Based upon an intelligence-limited model we consider the load distribution and the congestion and cascade failures due to attacks and occasional damages. It is found that the eigenvector centrality (EC) is an effective measure to find key nodes for traffic flow processes. The influence of structure of a WS small-world network is investigated in detail.

cond-mat.dis-nn↗

arXiv2005

Scaling Invariance in Spectra of Complex Networks: A Diffusion Factorial Moment Approach

A new method called diffusion factorial moment (DFM) is used to obtain scaling features embedded in spectra of complex networks. For an Erdos-Renyi network with connecting probability $p_{ER} < \frac{1}{N}$, the scaling parameter is $δ= 0.51$, while for $p_{ER} \ge \frac{1}{N}$ the scaling parameter deviates from it significantly. For WS small-world networks, in the special region $p_r \in [0.05,0.2]$, typical scale invariance is found. For GRN networks, in the range of $θ\in[0.33,049]$, we have $δ=0.6\pm 0.1$. And the value of $δ$ oscillates around $δ=0.6$ abruptly. In the range of $θ\in[0.54,1]$, we have basically $δ>0.7$. Scale invariance is one of the common features of the three kinds of networks, which can be employed as a global measurement of complex networks in a unified way.

cond-mat.stat-mech↗

arXiv2005

Complex Network Approach to Human Promoter Sequences

Based upon the correlation matrix of the human promoter sequences, a complex network is constructed to capture the principal relationships between these promoters. It is a complex network has the properties of the right-skewed degree distribution and the clustering simultaneously, i.e., a hierarchical structure. An eigenvector centrality (EC) based method is used to reconstruct this hierarchical structure.

q-bio.GN↗

arXiv2005

Reconstruct the Hierarchical Structure in a Complex Network

A number of recent works have concentrated on a few statistical properties of complex networks, such as the clustering, the right-skewed degree distribution and the community, which are common to many real world networks. In this paper, we address the hierarchy property sharing among a large amount of networks. Based upon the eigenvector centrality (EC) measure, a method is proposed to reconstruct the hierarchical structure of a complex network. It is tested on the Santa Fe Institute collaboration network, whose structure is well known. We also apply it to a Mathematicians' collaboration network and the protein interaction network of Yeast. The method can detect significantly hierarchical structures in these networks.

physics.soc-ph↗

arXiv2004

Temporal Series Analysis Approach to Spectra of Complex Networks

The spacing of nearest levels of the spectrum of a complex network can be regarded as a time series. Joint use of Multi-fractal Detrended Fluctuation Approach (MF-DFA) and Diffusion Entropy (DE) is employed to extract characteristics from this time series. For the WS (Watts and Strogatz) small-world model, there exist a critical point at rewiring probability . For a network generated in the range, the correlation exponent is in the range of . Above this critical point, all the networks behave similar with that at . For the ER model, the time series behaves like FBM (fractional Brownian motion) noise at . For the GRN (growing random network) model, the values of the long-range correlation exponent are in the range of . For most of the GRN networks the PDF of a constructed time series obeys a Gaussian form. In the joint use of MF-DFA and DE, the shuffling procedure in DE is essential to obtain a reliable result. PACS number(s): 89.75.-k, 05.45.-a, 02.60.-x

cond-mat.stat-mech↗