选择模型的理论扩展分析

选择模型为解释指数律、幂律的成因提供了基于偏好的概率分析方法.通过对选择试验的研究,在视野规律和选项分布之间导出了一对变换,称其为偏序变换.对偏序变换连续化,导出了对应的连续变换.仿照离散分布,定义了连续分布的比较函数,并沟通了离散和连续分布的伴随关系.通过选择模型,对幂律访问现象进行了仿真.选择理论对于复杂巨系统和社会行为的研究具有较重要的借鉴作用,可扩展统计物理学和概率论的理论基础. 关键词： 选择模型 幂律现象 偏序变换 比较函数     

选择理论生成幂律的扩展性研究

应用选择理论可以解释人类行为导致的幂律分布现象.通过对选择理论合理而简单的扩展,发现了更多幂律生成机制.研究表明选择理论对于单调的门限分布规律体现出生成机制的一般性;此类机制的扩展对幂律成因的深入研究具有较强的理论意义. 关键词： 选择理论 幂律分布 门限规律     

一种新型电力网络局域世界演化模型

现实世界中的许多系统都可以用复杂网络来描述,电力系统是人类创造的最为复杂的网络系统之一.当前经典的网络模型与实际电力网络存在较大差异.从电力网络本身的演化机理入手,提出并研究了一种可以模拟电力网络演化规律的新型局域世界网络演化模型.理论分析表明该模型的度分布具有幂尾特性,且幂律指数在3—∞之间可调.最后通过对中国北方电网和美国西部电网的仿真以及和无标度网络、随机网络的对比,验证了该模型可以很好地反映电力网络的演化规律,并且进一步证实了电力网络既不是无标度网络,也不是完全的随机网络. 关键词： 电力网络 演化模型 局域世界 幂律分布     

一个人类行为动力学模型及其精确解

对于Baraba′si提出的基于人类行为的排队模型,比较了Baraba′si的解与Va′zquez的解,指出这两个解存在差异.提出一个服务台具有服务时间的人类行为动力学模型.通过严格的解析,计算出事件的等待时间分布,得到事件在系统中的逗留时间分布(这个指标在Baraba′si模型中没有被获得).结果表明,按照优先权选择协议,这个模型的等待时间分布是指数γ=2的幂律分布,这与钱学森通信回复时间分布的指数2.1非常接近.     

新节点的边对网络无标度性影响

分析新节点边对网络无标度性的影响.虽然亚线性增长网络瞬态平均度分布尾部表现出了幂律分布性质,但是,这个网络的稳态度分布并不是幂律分布,由此可见,计算机模拟预测不出网络稳态度分布,它只能预测网络的瞬态度分布.进而建立随机增长网络模型,利用随机过程理论得到了这个模型的度分布的解析表达式,结果表明这个网络是无标度网络. 关键词： 复杂网络 无标度网络 小世界网络 度分布     

考虑人类流动行为的动态复杂网络研究

现实的复杂网络往往具有动态的结构特征.考虑人类流动行为的特点,提出一种随机行走网络模型对人类流动网络进行模拟研究.从度分布、聚类系数、最短路径距离以及位移分布等方面对该模型进行模拟分析,结果表明,该动态复杂网络度分布服从泊松分布,呈现随机网络特征;当通信半径大于某一较小数值时,具有高的聚类系数和短的平均路径长度,呈现小世界网络特征;而位移分布则满足幂律分布,这一结论与近年来人们对人类流动行为的实证研究结果相符合.     

幂律指数在1与3之间的一类无标度网络

借助排队系统中顾客批量到达的概念，提出节点批量到达的Poisson网络模型.节点按照到达率为λ的Poisson过程批量到达系统.模型1，批量按照到达批次的幂律非线性增长，其幂律指数为θ(0≤θ<+∞).BA模型是在θ＝0时的特例.利用Poisson过程理论和连续化方法进行分析，发现这个网络稳态平均度分布是幂律分布，而且幂律指数在1和3之间.模型2，批量按照节点到达批次的对数非线性增长，得出当批量增长较缓慢时，稳态度分布幂律指数为3.因此，节点批量到达的Poisson网络模型不仅是BA模型的推广，也为许多幂律指数在1和2之间的现实网络提供了理论依据.     

复杂网络局部结构的涌现:共同邻居驱动网络演化

在对真实网络的小世界和无标度特性进行了大量深入考量之后,最近的研究热点开始转移到更加细致的局部结构.实证数据显示,大量真实网络具有幂律的低阶集团度分布.这一普适的规律,无法由富者愈富以及熟人推荐的网络生长机理再现.本文提出一种由共同邻居驱动的网络演化模型,该模型能够重现实证研究所观察到的幂律集团度分布,暗示共同邻居驱动是复杂网络局部结构涌现形成的内在机理. 关键词： 复杂网络 演化模型 集团度分布 共同邻居     

复杂计算机网络中的相变和整体关联行为

利用相变的概念解释复杂计算机网络中广泛存在的幂律现象,并未考虑到网络这一复杂系统中节点之间的相互关联作用.通过一种计算机网络模型,对网络中的相变和整体关联行为进行了分析和比较.研究表明,随着负载的增加,网络节点行为的空间和时间的长程相关性逐渐增强,整体关联行为逐渐显现;在临界状态,网络负载变化的功率谱呈现1/f²的特点,而节点排队长度变化的功率谱服从1/f^α分布,其中α≤2.网络节点行为在时间和空间上的关联进一步得到说明. 关键词： 计算机网络 相变 关联 幂律     

在线社交网络中群体互动行为的时间特征

针对在线社交网络中群体互动行为,运用有偏扩散理论,构建有偏扩散模型,从任务的间隔时间τ与执行时间θ的关系视角,系统地阐述了群体互动活动中群体发帖行为与事件行为的时间间隔分布特征.针对群体用户发帖时τ≥θ的特征,论证群体用户发帖时间间隔的幂律分布机理.针对事件时τ>>θ的特征,论证了事件时间间隔具有指数效应的幂律分布机理,实验结果与理论推导吻合.     

Weblog patterns and human dynamics with decreasing interest

In order to describe the phenomenon that people’s interest in doing something always keep high in the beginning while gradually decreases until reaching the balance, a model which describes the attenuation of interest is proposed to reflect the fact that people’s interest becomes more stable after a long time. We give a rigorous analysis on this model by non-homogeneous Poisson processes. Our analysis indicates that the interval distribution of arrival-time is a mixed distribution with exponential and power-law feature, which is a power law with an exponential cutoff. After that, we collect blogs in ScienceNet.cn and carry on empirical study on the interarrival time distribution. The empirical results agree well with the theoretical analysis, obeying a special power law with the exponential cutoff, that is, a special kind of Gamma distribution. These empirical results verify the model by providing an evidence for a new class of phenomena in human dynamics. It can be concluded that besides power-law distributions, there are other distributions in human dynamics. These findings demonstrate the variety of human behavior dynamics.     

Herding and clustering: Ewens vs. Simon–Yule models

Clustering has often described by Ewens Sampling Formula (ESF). Focusing the attention on the evergreen problem of the size of firms, we discuss the compatibility of empirical data and ESF. In order to obtain a power law for all sizes in the present paper we shall explore the route inspired by Yule, Zipf and Simon. It differs from the Ewens model both for destruction and creation. In particular the probability of herding is independent on the size of the herd. Computer simulations seem to confirm that actually the mean number of clusters of size i (the equilibrium distribution) follows the corresponding Yule distribution. Finally we introduce a finite Markov chain, that resembles the marginal dynamics of a cluster, which drives the cluster to a censored Yule distribution.     

Relationship between degree-rank distributions and degree distributions of complex networks

Both the degree distribution and the degree-rank distribution, which is a relationship function between the degree and the rank of a vertex in the degree sequence obtained from sorting all vertices in decreasing order of degree, are important statistical properties to characterize complex networks. We derive an exact mathematical relationship between degree-rank distributions and degree distributions of complex networks. That is, for arbitrary complex networks, the degree-rank distribution can be derived from the degree distribution, and the reverse is true. Using the mathematical relationship, we study the degree-rank distributions of scale-free networks and exponential networks. We demonstrate that the degree-rank distributions of scale-free networks follow a power law only if scaling exponent λ>2. We also demonstrate that the degree-rank distributions of exponential networks follow a logarithmic law. The simulation results in the BA model and the exponential BA model verify our results.     

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.     

Re-inventing Willis

Scientists often re-invent things that were long known. Here we review these activities as related to the mechanism of producing power law distributions, originally proposed in 1922 by Yule to explain experimental data on the sizes of biological genera, collected by Willis. We also review the history of re-invention of closely related branching processes, random graphs and coagulation models.     

Empirical analysis of individual popularity and activity on an online music service system

Quantitative understanding of human behaviors supplies basic comprehension of the dynamics of many socio-economic systems. Based on the log data of an online music service system, we investigate the statistical characteristics of individual activity and popularity, and find that the distributions of both of them follow a stretched exponential form which interpolates between exponential and power law distribution. We also study the human dynamics on the online system and find that the distribution of interevent time between two consecutive listenings of music shows the fat tail feature. Besides, with the reduction of user activity the fat tail becomes more and more irregular, indicating different behavior patterns for users with diverse activities. The research results may shed some light on the in-depth understanding of collective behaviors in socio-economic systems.     

Heterogenous scaling in the inter-event time of on-line bookmarking

In this paper, we study the statistical properties of bookmarking behaviors in Delicious.com. We find that the inter-event time (τ) distributions of bookmarking decay in a power-like manner as τ increases at both individual and population levels. Remarkably, we observe a significant change in the exponent when the inter-event time increases from the intra-day range to the inter-day range. In addition, the dependence of the exponent on individual activity is found to be different in the two ranges. Instead of monotonically increasing with activity, the inter-day exponent peaks around 3. These results suggest that the mechanisms driving human actions are different in the intra-day and inter-day ranges. We further show that the global distributions of less active users are closer to an exponential distribution than those of more active users. Moreover, a universal behavior in the inter-day range is observed by considering the rescaled variable τ/〈τ〉. Finally, the possible causes of these phenomena are discussed.     

How people make friends in social networking sites—A microscopic perspective

We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters/accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks.