Conditions for the emergence of scaling in the inter-event time of uncorrelated and seasonal systems

Inter-event times have been studied across various disciplines in search for correlations. In this paper, we show analytical and numerical evidence that at the population level a power-law can be obtained by assuming Poissonian agents with different characteristic times, and at the individual level by assuming Poissonian agents that change the rates at which they perform an event in a random or deterministic fashion. The range in which we expect to see this behavior and the possible deviations from it are studied by considering the shape of the rate distribution.     

Human comment dynamics in on-line social systems

Human comment is studied using data from ‘tianya’ which is one of the most popular on-line social systems in China. We found that the time interval between two consecutive comments on the same topic, called inter-event time, follows a power-law distribution. This result shows that there is no characteristic decay time on a topic. It allows for very long periods without comments that separate bursts of intensive comments. Furthermore, the frequency of a different ID commenting on a topic also follows a power-law distribution. It indicates that there are some “hubs” in the topic who lead the direction of the public opinion. Based on the personal comments habit, a model is introduced to explain these phenomena. The numerical simulations of the model fit well with the empirical results. Our findings are helpful for discovering regular patterns of human behavior in on-line society and the evolution of the public opinion on the virtual as well as real society.     

Continuous time random walk with jump length correlated with waiting time

A coupled continuous time random walk (CTRW) model is proposed, in which the jump length of a walker is correlated with waiting time. The power law distribution is chosen as the probability density function of waiting time and the Gaussian-like distribution as the probability density function of jump length. Normal diffusion, subdiffusion and superdiffusion can be realized within the present model. It is shown that the competition between long-tailed distribution and correlation of jump length and waiting time will lead to different diffusive behavior.     

Allometric scaling of countries

As huge complex systems consisting of geographic regions, natural resources, people and economic entities, countries follow the allometric scaling law which is ubiquitous in ecological, and urban systems. We systematically investigated the allometric scaling relationships between a large number of macroscopic properties and geographic (area), demographic (population) and economic (GDP, gross domestic production) sizes of countries respectively. We found that most of the economic, trade, energy consumption, communication related properties have significant super-linear (the exponent is larger than 1) or nearly linear allometric scaling relations with the GDP. Meanwhile, the geographic (arable area, natural resources, etc.), demographic (labor force, military age population, etc.) and transportation-related properties (road length, airports) have significant and sub-linear (the exponent is smaller than 1) allometric scaling relations with area. Several differences of power law relations with respect to the population between countries and cities were pointed out. First, population increases sub-linearly with area in countries. Second, the GDP increases linearly in countries but not super-linearly as in cities. Finally, electricity or oil consumption per capita increases with population faster than cities.     

Empirical analysis of online human dynamics

Patterns of human activities have attracted increasing academic interests, since the quantitative understanding of human behavior is helpful to uncover the origins of many socioeconomic phenomena. This paper focuses on behaviors of Internet users. Six large-scale systems are studied in our experiments, including the movie-watching in Netflix and MovieLens, the transaction in Ebay, the bookmark-collecting in Delicious, and the posting in FreindFeed and Twitter. Empirical analysis reveals some common statistical features of online human behavior: (1) The total number of user’s actions, the user’s activity, and the interevent time all follow heavy-tailed distributions. (2) There exists a strongly positive correlation between user’s activity and the total number of user’s actions, and a significantly negative correlation between the user’s activity and the width of the interevent time distribution. We further study the rescaling method and show that this method could to some extent eliminate the different statistics among users caused by the different activities, yet the effectiveness depends on the data sets.     

Human dynamics scaling characteristics for aerial inbound logistics operation

In recent years, the study of power-law scaling characteristics of real-life networks has attracted much interest from scholars; it deviates from the Poisson process. In this paper, we take the whole process of aerial inbound operation in a logistics company as the empirical object. The main aim of this work is to study the statistical scaling characteristics of the task-restricted work patterns. We found that the statistical variables have the scaling characteristics of unimodal distribution with a power-law tail in five statistical distributions — that is to say, there obviously exists a peak in each distribution, the shape of the left part closes to a Poisson distribution, and the right part has a heavy-tailed scaling statistics. Furthermore, to our surprise, there is only one distribution where the right parts can be approximated by the power-law form with exponent α=1.50. Others are bigger than 1.50 (three of four are about 2.50, one of four is about 3.00). We then obtain two inferences based on these empirical results: first, the human behaviors probably both close to the Poisson statistics and power-law distributions on certain levels, and the human-computer interaction behaviors may be the most common in the logistics operational areas, even in the whole task-restricted work pattern areas. Second, the hypothesis in Vázquez et al. (2006) [A. Vázquez, J. G. Oliveira, Z. Dezsö, K.-I. Goh, I. Kondor, A.-L. Barabási. Modeling burst and heavy tails in human dynamics, Phys. Rev. E 73 (2006) 036127] is probably not sufficient; it claimed that human dynamics can be classified as two discrete university classes. There may be a new human dynamics mechanism that is different from the classical Barabási models.     

Opinion dynamics in networks with heterogeneous confidence and influence

We propose a discrete-time model of opinion dynamics. The neighborhood relationship is decided by confidence radius and influence radius of each agent. We investigate the influence of heterogeneity in confidence/influence distribution on the behavior of the network. The simulations suggest that the heterogeneity of single confidence or influence networks can promote the opinions to achieve consensus. It is shown that the heterogeneous influence radius systems converge in fewer time steps and more often in finite time than the heterogeneous confidence radius systems. We find that heterogeneity does not always promote consensus, and there is an optimal heterogeneity so that the relative size of the largest consensus cluster reaches maximum in heterogeneous confidence and influence networks.     

Fluctuation scaling of quotation activities in the foreign exchange market

We study the scaling behavior of quotation activities for various currency pairs in the foreign exchange market. The components’ centrality is estimated from multiple time series and visualized as a currency pair network. The power-law relationship between a mean of quotation activity and its standard deviation for each currency pair is found. The scaling exponent α and the ratio between common and specific fluctuations η increase with the length of the observation time window . The result means that although for , the market dynamics are governed by specific processes, and at a longer time scale the common information flow becomes more important. We point out that quotation activities are not independently Poissonian for , and temporally or mutually correlated activities of quotations can happen even at this time scale. A stochastic model for the foreign exchange market based on a bipartite graph representation is proposed.     

电子商务中人类活动的标度行为实证研究

应用去趋势波动分析法,对电子商务中人类网上购物行为进行研究,首次探讨了人类浏览及购买行为时间序列(数量波动)标度律.首先,研究发现人类网上购物行为呈现出明显的周期性,其时间序列的概率密度函数具有显著的双模态特征.其次,利用傅里叶变换方法分析浏览以及购买行为时间序列的功率谱,发现其演化过程不同于无关联的泊松过程.最后,基于功率谱过滤周期性趋势的影响,对去除周期趋势后的浏览和购买行为时间序列进行去趋势波动分析,发现其标度行为表明其具有较强的长程关联特性,且平均标度值近似为1,表明其具有自组织临界性.实证研究结果与其他领域如因特网交通流和金融市场价格波动的标度行为相似,有助于理解人类活动如何影响电子商务系统演化和提高在线商务活动效率,对分析电子商务中人类行为活动的机制和预测其波动趋势具有重要的启示作用.     

Statistical dynamics of early river networks

Based on local erosion rule and fluctuations in rainfall, geology and parameters of a river channel, a generalized Langevin equation is proposed to describe the random prolongation of a river channel. This equation is transformed into the Fokker–Plank equation to follow the early evolution of a river network and the variation of probability distribution of channel lengths. The general solution of the equation is in the product form of two terms. One term is in power form and the other is in exponent form. This distribution shows a complete history of a river network evolving from its infancy to “adulthood”). The infancy is characterized by the Gaussian distribution of the channel lengths, while the adulthood is marked by a power law distribution of the channel lengths. The variation of the distribution from the Gaussian to the power law displays a gradual developing progress of the river network. The distribution of basin areas is obtained by means of Hack’s law. These provide us with new understandings towards river networks.     

Cycles,scaling and crossover phenomenon in length of the day (LOD) time series

The dynamics of the temporal fluctuations of the length of the day (LOD) time series from January 1, 1962 to November 2, 2006 were investigated. The power spectrum of the whole time series has revealed annual, semi-annual, decadal and daily oscillatory behaviors, correlated with oceanic–atmospheric processes and interactions. The scaling behavior was analyzed by using the detrended fluctuation analysis (DFA), which has revealed two different scaling regimes, separated by a crossover timescale at approximately 23 days. Flicker-noise process can describe the dynamics of the LOD time regime involving intermediate and long timescales, while Brownian dynamics characterizes the LOD time series for small timescales.     

On the irreconcilability of Pareto and Gibrat laws

If business firms face a multiplicative growth process in which their growth rates are Laplace distributed and independent from their sizes, the size cannot be distributed according to a stationary Pareto distribution. Recent contributions, using formal arguments, seem to contrast with these statements. We prove that the proposed formal results are wrong.     

Effects of the turnover rate on the size distribution of firms: An application of the kinetic exchange models

We address the issue of the distribution of firm size. To this end we propose a model of firms in a closed, conserved economy populated with zero-intelligence agents who continuously move from one firm to another. We then analyze the size distribution and related statistics obtained from the model. There are three well known statistical features obtained from the panel study of the firms i.e., the power law in size (in terms of income and/or employment), the Laplace distribution in the growth rates and the slowly declining standard deviation of the growth rates conditional on the firm size. First, we show that the model generalizes the usual kinetic exchange models with binary interaction to interactions between an arbitrary number of agents. When the number of interacting agents is in the order of the system itself, it is possible to decouple the model. We provide exact results on the distributions which are not known yet for binary interactions. Our model easily reproduces the power law for the size distribution of firms (Zipf’s law). The fluctuations in the growth rate falls with increasing size following a power law (though the exponent does not match with the data). However, the distribution of the difference of the firm size in this model has Laplace distribution whereas the real data suggests that the difference of the log of sizes has the same distribution.     

The scaling of human mobility by taxis is exponential

As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users’ locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis’ traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities.     

Quantifying price fluctuations in the Brazilian stock market

This paper investigates price fluctuations in the Brazilian stock market. We employ a recently developed methodology to test whether the Brazilian stock price returns present a power law distribution and find that we cannot reject such behavior. Empirical results for sub-partitions of the time series suggests that for most of the time the power law is not rejected, but that in some cases the data set does not conform with a power law distribution.     

通过理论定标律研究激光间接驱动的参数空间

系统地梳理了激光间接驱动点火靶内爆压缩的物理过程,使用理论方法和一维流体力学模拟给出了靶丸内爆过程中的关键定标律公式。通过这些定标律公式获得了在给定黑腔辐射温度、飞行熵增因子、整形速度和烧蚀材料的条件下,靶丸装量——半径参数空间的点火岛区域。研究了靶丸性能参数随辐射温度、飞行熵增因子等的变化规律：当靶丸所处黑腔辐射温度升高时,内爆的稳定性将变好;设计上在靶丸装量不变的条件下,靶丸半径需要减小。当靶丸的飞行熵增因子增大时,内爆增益略微减小,内爆稳定性变好;但是点火阈值因子减小导致点火岛的区域变窄。当靶丸的整形速度增大时,点火岛的区域略微变大,内爆稳定性变化不显著;设计上在靶丸装量不变的条件下,需要增大靶丸半径,这会导致靶丸壳层形状因子变大。当改变靶丸烧蚀材料,提高质量烧蚀速率与烧蚀压时,能量增益变大且稳定性增强;设计上在靶丸装量不变的条件下,需要减小靶丸半径。     

Multifractal behavior of wild-land and forest fire time series in Brazil

We examine statistical properties of a daily hot pixel time series recorded in Brazil during the period 1998–2006, using Multifractal Detrended Fluctuation Analysis (MF-DFA). We find that generalized scaling exponent h(q)$h\left(q\right)$ is a decreasing function of q$q$, indicating multifractal behavior of hot pixel dynamics. We also calculate multifractal spectra f(α)$f\left(\alpha \right)$ and use fourth-degree polynomial regression to estimate complexity parameters that describe the degree of multifractality of the underlying process. After July 2002, when a significant increase of the number of hot pixel observations is recorded, the complexity of the series is reduced (manifested by the reduction of width of the f(α)$f\left(\alpha \right)$ spectrum), while small fluctuations increase their dominance over large scale fluctuations (manifested by the increase of skew parameter r$r$). These results should be taken into account when devising ecological and climatic models for Brazil, that contemplate the phenomena of wild-land and forest fires.