Showing papers in "Physica A-statistical Mechanics and Its Applications in 2008"
TL;DR: Several questions about economic time series are connected to the understanding of the behavior of key variables at different frequencies over time, but this type of information is difficult to uncover using pure time-domain or pure frequency-domain methods.
Abstract: Central banks have different objectives in the short and long run. Governments operate simultaneously at different timescales. Many economic processes are the result of the actions of several agents, who have different term objectives. Therefore, a macroeconomic time series is a combination of components operating on different frequencies. Several questions about economic time series are connected to the understanding of the behavior of key variables at different frequencies over time, but this type of information is difficult to uncover using pure time-domain or pure frequency-domain methods. To our knowledge, for the first time in an economic setup, we use cross-wavelet tools to show that the relation between monetary policy variables and macroeconomic variables has changed and evolved with time. These changes are not homogeneous across the different frequencies. c 2008 Elsevier B.V. All rights reserved.
459 citations
TL;DR: It is shown that the degree distribution in this network has a power-law degree distribution k−5 and that the probability that two customers are connected by a link follows a gravity model, i.e. decreases as d−2, where d is the distance between the customers.
Abstract: In this paper, we analyze statistical properties of a communication network constructed from the records of a mobile phone company. The network consists of 2.5 million customers that have placed 810 million communications (phone calls and text messages) over a period of 6 months and for whom we have geographical home localization information. It is shown that the degree distribution in this network has a power-law degree distribution k(-5) and that the probability that two customers are connected by a link follows a gravity model, i.e. decreases as d(-1). where d is the distance between the customers. We also consider the geographical extension of communication triangles and we show that communication triangles are not only composed of geographically adjacent nodes but that they may extend over large distances. This last property is not captured by the existing models of geographical networks and in a last section we propose a new model that reproduces the observed property. Our model, which is based on the migration and on the local adaptation of agents, is then studied analytically and the resulting predictions are confirmed by computer simulations. (C) 2008 Elsevier B.V. All rights reserved.
444 citations
TL;DR: The Airport Network of India (ANI) is found to be a small-world network characterized by a truncated power-law degree distribution and has a signature of hierarchy, indicating possible mechanism of formation of a national transportation network, which is different from that on a global scale.
Abstract: Transportation infrastructure of a country is one of the most important indicators of its economic growth. Here we study the Airport Network of India (ANI) which represents India’s domestic civil aviation infrastructure as a complex network. We find that ANI, a network of domestic airports connected by air links, is a small-world network characterized by a truncated power-law degree distribution and has a signature of hierarchy. We investigate ANI as a weighted network to explore its various properties and compare them with their topological counterparts. The traffic in ANI, as in the World-wide Airport Network (WAN), is found to be accumulated on interconnected groups of airports and is concentrated between large airports. In contrast to WAN, ANI is found to be having disassortative mixing which is offset by the traffic dynamics. The analysis indicates possible mechanism of formation of a national transportation network, which is different from that on a global scale.
396 citations
TL;DR: In this paper, the average consensus problem in directed networks of agents with both switching topology and time-delay is studied and the stability analysis is performed based on a proposed Lyapunov-Krasovskii function.
Abstract: This paper is devoted to the study of the average-consensus problem in directed networks of agents with both switching topology and time-delay. The stability analysis is performed based on a proposed Lyapunov–Krasovskii function. Sufficient conditions in terms of linear matrix inequalities (LMIs) are given to guarantee the average consensus under arbitrary switching of the network topology even if the time-delay is time-varying. Numerical simulations show the effectiveness of our theoretical results.
354 citations
TL;DR: This paper proposes in this paper a reliable procedure for constructing complex networks from the correlation matrix of a time series, an original stock timeseries, the corresponding return series and its amplitude series.
Abstract: Recent works show that complex network theory may be a powerful tool in time series analysis. We propose in this paper a reliable procedure for constructing complex networks from the correlation matrix of a time series. An original stock time series, the corresponding return series and its amplitude series are considered. The degree distribution of the original series can be well fitted with a power law, while that of the return series can be well fitted with a Gaussian function. The degree distribution of the amplitude series contains two asymmetric Gaussian branches. Reconstruction of networks from time series is a common problem in diverse research. The proposed strategy may be a reasonable solution to this problem.
316 citations
TL;DR: A detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving Average (CMA) Method and a Modified Detrended Fluctuation Analysis (MDFA) is presented, finding that CMA performs the same as DFA in long data with weak trends and is slightly superior to D FA in short data with strong trends.
Abstract: We examine several recently suggested methods for the detection of long-range correlations in data series based on similar ideas as the well-established Detrended Fluctuation Analysis (DFA). In particular, we present a detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving Average (CMA) Method and a Modified Detrended Fluctuation Analysis (MDFA). We find that CMA performs the same as DFA in long data with weak trends and is slightly superior to DFA in short data with weak trends. When comparing standard DFA to MDFA we observe that DFA performs slightly better in almost all examples we studied. We also discuss how several types of trends affect different types of DFA. For weak trends in the data, the new methods are comparable with DFA in these respects. However, if the functional form of the trend in data is not a-priori known, DFA remains the method of choice. Only a comparison of DFA results, using different detrending polynomials, yields full recognition of the trends. A comparison with independent methods is recommended for proving long-range correlations.
283 citations
TL;DR: It is found that the majority of existing links are associated to weak trade relationships; the weighted WTW is only weakly disassortative; and countries holding more intense trade relationships are more clustered.
Abstract: This paper studies the topological properties of the World Trade Web (WTW) and its evolution over time by employing a weighted-network analysis. We show that the WTW, viewed as a weighted network, displays statistical features that are very different from those obtained by using a traditional binary-network approach. In particular, we find that: (i) the majority of existing links are associated to weak trade relationships; (ii) the weighted WTW is only weakly disassortative; (iii) countries holding more intense trade relationships are more clustered.
280 citations
TL;DR: In this paper, the authors study the transition towards effective payoffs in the prisoner's dilemma game on scale-free networks by introducing a normalization parameter guiding the system from accumulated payoffs to payoffs normalized with the connectivity of each agent.
Abstract: We study the transition towards effective payoffs in the prisoner’s dilemma game on scale-free networks by introducing a normalization parameter guiding the system from accumulated payoffs to payoffs normalized with the connectivity of each agent. We show that during this transition the heterogeneity-based ability of scale-free networks to facilitate cooperative behavior deteriorates continuously, eventually collapsing with the results obtained on regular graphs. The strategy donations and adaptation probabilities of agents with different connectivities are studied. Results reveal that strategies generally spread from agents with larger towards agents with smaller degree. However, this strategy adoption flow reverses sharply in the fully normalized payoff limit. Surprisingly, cooperators occupy the hubs even if the averaged cooperation level due to partly normalized payoffs is moderate.
279 citations
TL;DR: In this article, the authors evaluated the multifractality degree in a collection of developed and emerging stock market indices and found that higher multifractal degree is associated with a less developed market.
Abstract: In this paper, the multifractality degree in a collection of developed and emerging stock market indices is evaluated. Empirical results suggest that the multifractality degree can be used as a quantifier to characterize the stage of market development of world stock indices. We develop a model to test the relationship between the stage of market development and the multifractality degree and find robust evidence that the relationship is negative, i.e., higher multifractality is associated with a less developed market. Thus, an inefficiency ranking can be derived from multifractal analysis. Finally, a link with previous volatility time series results is established.
270 citations
TL;DR: In this article, a controller based on active sliding mode theory is proposed to synchronize chaotic fractional-order systems in master-slave structure, where master and slave systems may be identical or different.
Abstract: In this paper, we propose a controller based on active sliding mode theory to synchronize chaotic fractional-order systems in master–slave structure. Master and slave systems may be identical or different. Based on stability theorems in the fractional calculus, analysis of stability is performed for the proposed method. Finally, three numerical simulations (synchronizing fractional-order Lu–Lu systems, synchronizing fractional order Chen–Chen systems and synchronizing fractional-order Lu–Chen systems) are presented to show the effectiveness of the proposed controller. The simulations are implemented using two different numerical methods to solve the fractional differential equations.
245 citations
TL;DR: A quantitative indicator of link persistence is introduced to explore the correlations between the structure of a mobile phone network and the persistence of its links and shows that reciprocity is the strongest predictor of tie persistence.
Abstract: The empirical study of network dynamics has been limited by the lack of longitudinal data. Here we introduce a quantitative indicator of link persistence to explore the correlations between the structure of a mobile phone network and the persistence of its links. We show that persistent links tend to be reciprocal and are more common for people with low degree and high clustering. We study the redundancy of the associations between persistence, degree, clustering and reciprocity and show that reciprocity is the strongest predictor of tie persistence. The method presented can be easily adapted to characterize the dynamics of other networks and can be used to identify the links that are most likely to survive in the future.
TL;DR: In this article, the analysis of long memory processes in capital markets has been one of the topics in finance, since the existence of the market memory could implicate the rejection of an efficient market hypothesis.
Abstract: The analysis of long memory processes in capital markets has been one of the topics in finance, since the existence of the market memory could implicate the rejection of an efficient market hypothesis. The study of these processes in finance is realized through Hurst exponent and the most classical method applied is R/S analysis. In this paper we will discuss the efficiency of this methodology as well as some of its more important modifications to detect the long memory. We also propose the application of a classical geometrical method with short modifications and we compare both approaches.
TL;DR: In this article, a four-dimensional hyperchaotic Lorenz system was obtained by adding a nonlinear controller to the Lorenz chaotic system, which is studied by bifurcation diagram, Lyapunov exponents spectrum and phase diagram.
Abstract: This paper presents a four-dimension hyperchaotic Lorenz system, obtained by adding a nonlinear controller to Lorenz chaotic system. The hyperchaotic Lorenz system is studied by bifurcation diagram, Lyapunov exponents spectrum and phase diagram. Numerical simulations show that the new system’s behavior can be convergent, divergent, periodic, chaotic and hyperchaotic when the parameter varies.
TL;DR: In this paper, the dynamic behavior of US stock markets is characterized on the basis of the temporal variations of the Hurst exponent estimated with detrended fluctuation analysis (DFA) over moving windows for the historical Dow Jones (1928-2007) and S&P-500 (1950-2007).
Abstract: In this work, the dynamical behavior of the US stock markets is characterized on the basis of the temporal variations of the Hurst exponent estimated with detrended fluctuation analysis (DFA) over moving windows for the historical Dow Jones (1928–2007) and the S&P-500 (1950–2007) daily indices According to the results drawn: (i) the Hurst exponent displays an erratic dynamics with some episodes alternating low and high persistent behavior, (ii) the major breakthrough of the long-term trend of the scaling behavior occurred in 1972, at the end of the Bretton Woods system, when the Hurst exponent shifted form a positive to a negative long-term trend Other effects, such as the 1987 crisis and the emergence of anti-correlated behavior in the recent two years, are also discussed
TL;DR: In this paper, the authors investigated the dispersion relation and Landau damping of ion acoustic waves in the collisionless magnetic-field-free plasma when the plasma is described by the nonextensive q -distributions of Tsallis statistics.
Abstract: We investigate the dispersion relation and Landau damping of ion acoustic waves in the collisionless magnetic-field-free plasma when the plasma is described by the nonextensive q -distributions of Tsallis statistics. We show that the increased numbers of superthermal particles and low velocity particles can explain the strengthened and weakened modes of Landau damping, respectively, with the q -distribution. When the ion temperature is equal to the electron temperature, the weakly damped waves are found to be the distributions with small values of q .
TL;DR: In this paper, the influence of an endoscope on the peristaltic flow of a couple stress fluid in an annulus under a zero Reynolds number and long wavelength approximation is discussed.
Abstract: This paper discusses the influence of an endoscope on the peristaltic flow of a couple stress fluid in an annulus under a zero Reynolds number and long wavelength approximation. The inner tube is uniform, rigid, while the outer tube has a sinusoidal wave traveling down its wall. Analytical expressions for the axial velocity, stream function and axial pressure gradient are established. The flow is investigated in a wave frame of reference moving with the velocity of the wave. Numerical calculations are carried out for the pressure rise, frictional forces and trapping. The features of the flow characteristics are analyzed by plotting graphs and discussed in detail.
TL;DR: It is found that the BA scale-free network reaches the strongest robustness level in the case of α=1 and the robustness of the network has a positive correlation with the average degree 〈k〉, where the robustity is quantified by a transition from normal state to collapse.
Abstract: In this paper, adopting the initial load of a node i to be a k i α with k i being the degree of the node i , we propose a cascading model based on a load local redistribution rule and examine cascading failures on the typical network, i.e., the BA network with the scale-free property. We find that the BA scale-free network reaches the strongest robustness level in the case of α = 1 and the robustness of the network has a positive correlation with the average degree 〈 k 〉 , where the robustness is quantified by a transition from normal state to collapse. In addition, we further discuss the effects of two different attacks for the robustness against cascading failures on our cascading model and find an interesting result, i.e., the effects of two different attacks, strongly depending to the value α . These results may be very helpful for real-life networks to avoid cascading-failure-induced disasters.
TL;DR: The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.
Abstract: Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients L C 4 and L C 3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.
TL;DR: It is argued that a complex graph contains many different subgraphs, and different measures that quantify this complexity are presented, for instance C1e, the relative number of non-isomorphic one-edge-deleted sub graphs (i.e. DECK size).
Abstract: Many papers published in recent years show that real-world graphs G ( n , m ) ( n nodes, m edges) are more or less “complex” in the sense that different topological features deviate from random graphs. Here we narrow the definition of graph complexity and argue that a complex graph contains many different subgraphs. We present different measures that quantify this complexity, for instance C 1 e , the relative number of non-isomorphic one-edge-deleted subgraphs (i.e. DECK size). However, because these different subgraph measures are computationally demanding, we also study simpler complexity measures focussing on slightly different aspects of graph complexity. We consider heuristically defined “product measures”, the products of two quantities which are zero in the extreme cases of a path and clique, and “entropy measures” quantifying the diversity of different topological features. The previously defined network/graph complexity measures Medium Articulation and Offdiagonal complexity (OdC) belong to these two classes. We study OdC measures in some detail and compare it with our new measures. For all measures, the most complex graph G C max has a medium number of edges, between the edge numbers of the minimum and the maximum connected graph n − 1 m ( G C max ) n ( n − 1 ) / 2 . Interestingly, for some measures C this number scales exactly with the geometric mean of the extremes: m ( G C max ) = n / 2 ( n − 1 ) ∼ n 1.5 . All graph complexity measures are characterized with the help of different example graphs. For all measures the corresponding time complexity is given. Finally, we discuss the complexity of 33 real-world graphs of different biological, social and economic systems with the six computationally most simple measures (including OdC). The complexities of the real graphs are compared with average complexities of two different random graph versions: complete random graphs (just fixed n , m ) and rewired graphs with fixed node degrees.
TL;DR: In this paper, an adaptive feedback technique is proposed to identify the exact topology of a weighted general complex dynamical network model with time-varying coupling delay by receiving the network nodes evolution.
Abstract: Many existing papers investigated the geometric features, control and synchronization of complex dynamical networks provided with certain topology. However, the exact topology of a network is sometimes unknown or uncertain. Based on LaSalle’s invariance principle, we propose an adaptive feedback technique to identify the exact topology of a weighted general complex dynamical network model with time-varying coupling delay. By receiving the network nodes evolution, the topology of such a kind of network with identical or different nodes, or even with varying topology can be monitored. In comparison with previous methods, time delay is taken into account in this simple, analytical and systematic synchronization-based technique. Particularly, the weight configuration matrix is not necessarily symmetric or irreducible, and the inner-coupling matrix need not be symmetric. Illustrative simulations are provided to verify the correctness and effectiveness of the proposed scheme.
TL;DR: Empirical analysis of statistical properties of two important Chinese online social networks—a blogging network and an SNS open to college students find that the blogging network shows disassortative mixing pattern, whereas the SNS network is an assortative one.
Abstract: Today the World Wide Web is undergoing a subtle but profound shift to Web 2.0, to become more of a social web. The use of collaborative technologies such as blogs and social networking site (SNS) leads to instant online community in which people communicate rapidly and conveniently with each other. Moreover, there are growing interest and concern regarding the topological structure of these new online social networks. In this paper, we present empirical analysis of statistical properties of two important Chinese online social networks—a blogging network and an SNS open to college students. They are both emerging in the age of Web 2.0. We demonstrate that both networks possess small-world and scale-free features already observed in real-world and artificial networks. In addition, we investigate the distribution of topological distance. Furthermore, we study the correlations between degree (in/out) and degree (in/out), clustering coefficient and degree, popularity (in terms of number of page views) and in-degree (for the blogging network), respectively. We find that the blogging network shows disassortative mixing pattern, whereas the SNS network is an assortative one. Our research may help us to elucidate the self-organizing structural characteristics of these online social networks embedded in technical forms.
TL;DR: In this article, a two velocity difference model for a car-following theory is put forward considering navigation in modern traffic, and the model is an improvement over the previous ones theoretically, because it considers more aspects in the car following process than others.
Abstract: In the light of the optimal velocity model, a two velocity difference model for a car-following theory is put forward considering navigation in modern traffic. To our knowledge, the model is an improvement over the previous ones theoretically, because it considers more aspects in the car-following process than others. Then we investigate the property of the model using linear and nonlinear analyses. The Korteweg–de Vries equation (for short, the KdV equation) near the neutral stability line and the modified Korteweg–de Vries equation (for short, the mKdV equation) around the critical point are derived by applying the reductive perturbation method. The traffic jam could be thus described by the KdV soliton and the kink–anti-kink soliton for the KdV equation and mKdV equation, respectively. Numerical simulations are made to verify the model, and good results are obtained with the new model.
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TL;DR: Temporal Graphs as discussed by the authors is a representation that encodes temporal data into graphs while fully retaining the temporal information of the original data, which can be used to explore the dynamic temporal properties of data by using existing graph algorithms.
Abstract: We introduce the idea of temporal graphs, a representation that encodes temporal data into graphs while fully retaining the temporal information of the original data This representation lets us explore the dynamic temporal properties of data by using existing graph algorithms (such as shortest-path), with no need for data-driven simulations We also present a number of metrics that can be used to study and explore temporal graphs Finally, we use temporal graphs to analyse real-world data and present the results of our analysis
TL;DR: In this paper, the authors consider overdamped diffusion processes driven out of thermal equilibrium and analyze their dynamical steady fluctuations, and highlight the canonical structure of the joint fluctuations of occupation times and currents.
Abstract: We consider overdamped diffusion processes driven out of thermal equilibrium and we analyze their dynamical steady fluctuations. We discuss the thermodynamic interpretation of the joint fluctuations of occupation times and currents; they incorporate respectively the time-symmetric and the time-antisymmetric sector of the fluctuations. We highlight the canonical structure of the joint fluctuations. The novel concept of traffic complements the entropy production for the study of the occupation statistics. We explain how the occupation and current fluctuations get mutually coupled out of equilibrium. Their decoupling close-to-equilibrium explains the validity of entropy production principles.
TL;DR: In this paper, the global synchronization for an array of nonlinearly coupled identical chaotic systems is investigated, and a distinctive feature of this work is to address synchronization issues for nonlinear coupled complex networks with an asymmetrical coupling matrix.
Abstract: In this paper, the global synchronization for an array of nonlinearly coupled identical chaotic systems is investigated. A distinctive feature of this work is to address synchronization issues for nonlinearly coupled complex networks with an asymmetrical coupling matrix. By projecting the nonlinear coupling function onto a linear one and assuming the difference between them as a disturbing function, we give some criteria for the global synchronization in virtual of the left eigenvector corresponding to the zero eigenvalue of the coupling matrix. Numerical examples are also provided to demonstrate the effectiveness of the theory.
TL;DR: Simulation results show that the proposed model can capture the basic characteristics of pedestrian evacuation and generate the average evacuation times reasonably when an emergency incident occurs in the building.
Abstract: We present a mobile lattice gas model for simulating the pedestrian evacuation process in a public building, through combining the advantages of the lattice gas model and the social force model. In our model, the interaction force between every two pedestrians and that between a pedestrian and the building wall are determined by the distance and the pedestrian’s moving step size. Simulation results show that the proposed model can capture the basic characteristics of pedestrian evacuation and generate the average evacuation times reasonably when an emergency incident occurs in the building.
TL;DR: By designing effective adaptive controllers, the theoretical analysis of synchronization between two complex networks with nonidentical topological structures is addressed and several useful criteria for synchronization are given.
Abstract: This paper addresses the theoretical analysis of synchronization between two complex networks with nonidentical topological structures. By designing effective adaptive controllers, we achieve synchronization between two complex networks. Both the cases of identical and nonidentical network topological structures are considered and several useful criteria for synchronization are given. Illustrative examples are presented to demonstrate the application of the theoretical results.
TL;DR: In this article, the stability analysis for a class of impulsive stochastic Cohen-Grossberg neural networks with mixed delays is considered, where mixed time delays comprise both the time-varying and infinite distributed delays.
Abstract: In this paper, the problem of stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M -matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p -stability of the equilibrium point for the addressed impulsive stochastic Cohen–Grossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.
TL;DR: In this article, the authors used cross-sectional data for 88 countries to investigate the nonlinear relationship between tourism development and economic growth when a threshold variable is used, defined as the degree of tourism specialization ( q i, defined as receipts from international tourism as a percentage of GDP).
Abstract: We use cross sectional data (1995–2005 yearly averages) for 88 countries to investigate the nonlinear relationship between tourism development and economic growth when a threshold variable is used. The degree of tourism specialization ( q i , defined as receipts from international tourism as a percentage of GDP) is used as the threshold variable. The results of the tests for nonlinearity indicate that the 88 countries’ data should be separated into three different groups or regimes to analyze the tourism-growth nexus. The results of the threshold regression show that when the q i is below 4.0488% (regime 1, 57 countries) or above 4.7337% (regime 3, 23 countries), there exists a significantly positive relationship between tourism growth and economic growth. However, when the q i is above 4.0488% and below 4.7337% (regime 2, 8 countries), we are unable to find evidence of such a significant relationship. Further in-depth analysis reveals that relatively low ratios of the value added of the service industry to GDP, and the forested area per country area are able to explain why we are unable to find a significant relationship between these two variables in regime 2’s countries.
TL;DR: The multi-grid model is improved by considering the pre-movement time of each pedestrian, adopting variable velocity and using a new update procedure to simulate the evacuation process and compare the simulation results with the experimental results, and find that they agree with each other closely.
Abstract: The evacuation process of students from a classroom is investigated by both experiment and modeling. We investigate the video record of the pedestrian movement in the classroom, and find some typical characteristics of the evacuation, including variable velocity, dislocable queuing, monopolizing exit and so on. Based on the experimental observation, we improve the multi-grid model by considering the pre-movement time of each pedestrian, adopting variable velocity and using a new update procedure. With the improved multi-grid model, we simulate the evacuation process and compare the simulation results with the experimental results, and find that they agree with each other closely. In order to analyze the uncertainty of evacuation, we investigate the influences of the pre-movement time and its distribution on the evacuation. It is found that the evacuation times exhibit a (truncated) normal distribution and vary within a region of about 30% of the mean value. An interesting phenomenon is that the evacuation time of the egress experiment is close to the minimum value calculated with the model, due to the coordination among pedestrians during the experiment. The study may be useful in developing applicable egress models and understanding the basic egress behaviors.