Percolation theory and fragmentation measures in social networks

We study the statistical properties of a recently proposed social networks measure of fragmentation F after removal of a fraction q of nodes or links from the network. The measure F   is defined as the ratio of the number of pairs of nodes that are not connected in the fragmented network to the total number of pairs in the original fully connected network. We compare this measure with the one traditionally used in percolation theory, _P∞$P_{\infty }$, the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods, we study Erd?s–Rényi (ER) and scale-free (SF) networks under various node removal strategies. We find that for a network obtained after removal of a fraction q   of nodes above criticality, _P∞≈(1-F)^1/2$P_{\infty }\approx (1-F{)}^{1/2}$. For fixed _P∞$P_{\infty }$ and close to criticality, we show that 1-F$1-F$ better reflects the actual fragmentation. For a given _P∞$P_{\infty }$, 1-F$1-F$ has a broad distribution and thus one can improve significantly the fragmentation of the network. We also study and compare the fragmentation measure F   and the percolation measure _P∞$P_{\infty }$ for a real national social network of workplaces linked by the households of the employees and find similar results.     

A stochastic model of tweet diffusion on the Twitter network

We introduce a stochastic model which describes the information diffusion on the network of a popular web service, Twitter. Restricting our attention to the daily diffusion from a popular account, we model the information diffusion as a random multiplicative process. We justify our model by directly observing the statistics of the multiplicative factors in the Twitter data.     

Disease dynamics in a dynamic social network

We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.     

Users structure and behavior on an online social network during a political protest

Over the past years, new technologies and specially online social networks have penetrated into the world’s population at an accelerated pace. In this paper we analyze collected data from the web application Twitter, in order to describe the structure and dynamics of the emergent social networks, based on complexity science. We focused on a Venezuelan protest that took place exclusively by Twitter during December, 2010. We found a community structure with highly connected hubs and three different kinds of user behavior that determine the information flow dynamics. We noticed that even though online social networks appear to be a pure social environment, traditional media still holds loads of influence inside the network.     

Method of analyzing the influence of network structure on information diffusion

Social phenomena are affected by the structure of networks consisting of personal relationships. In the present paper, the diffusion of information among people is examined. In particular, the relationship between the network structure and the dynamics is studied. First, several networks are generated using the proposed network model and other network models, such as the WS model and the KE model. By changing the parameters of the network models, networks with different structures are generated. The parameters of the network models determine the topology of the networks and the statistical indicators.     

社交网络中热点话题深度挖掘方法及实验测试

互联网大数据时代,社交网络数据交互具有实时性、社会性、复杂性。多元架构数据信息中会存在热点话题数据。传统社交网络热点话题挖掘方法存在热点词条检索速度慢、话题词条层浅、断层数据无法挖掘等一系列问题,如何将社交网络中的热点话题数据进行挖掘,针对这一问题提出大数据信息词条特征比对提取方法,对社交网络中的大数据数据信息词条进行特征显化处理,采用饼图对比方式对特征化词条数据进行网络互交频率展现,采用多维数据获取法,解决社交网络热点话题挖掘中出现的数据阻滞现象,满足社交网络中热点话题深度挖掘的要求。通过仿真实验对提出方法进行效率、准确度、速度测试,实验结果表明,提出方法对社交网络中的热点话题挖掘快捷、高效、实用性强。     

红外光谱中取代基对苯环骨架振动吸收峰的影响规律

本文初步地探讨了单取代及对位双取代对苯环骨架振动吸收峰频率和强度的影响规律 ,得出结论 :1给电子单取代基随给电子能力增强使苯环骨架振动吸收峰 16 0 0 cm-1增强 ,而吸电子基则相反。 2卤素作为单取代基时 ,使苯环骨架伸缩振动产生强红外吸收峰 ;且随卤素电负性增强吸收峰频率向高波数移动。 3对位取代基为不同类型时 ,16 0 0 cm-1处显强峰 ,而为同一类型时 ,当电子效应相近或相同时 ,16 0 0 cm-1处吸收峰很弱或根本无吸收峰。并讨论了产生这些现象的原因。     

一种可大范围调节聚类系数的加权无标度网络模型

在Barrat, Barthélemy 和 Vespignani (BBV)加权无标度网络模型的基础上，提出了一种可大范围调节聚类系数的加权无标度网络模型——广义BBV模型(GBBV模型).理论分析和仿真实验表明，GBBV模型保留了BBV模型的许多特征，节点度、节点权重和边权值等都服从幂律分布.但是，GBBV模型克服了BBV模型只能小范围调节聚类系数的缺陷，从而可以用于具有大聚类系数网络的建模. 关键词： 无标度网络 加权网络 聚类系数     

Penrose tiling fractality in coordination Cayley’s tree graphs representation

We offer the mathematical apparatus for mapping lattice and cellular systems into the generalized coordination Cayley’s tree graphs. These Cayley’s trees have a random branchiness property and an intralayer interbush local intersection. Classical Bethe-Cayley tree graphs don’t have these properties. Bush type simplicial decomposition on Cayley’s tree graphs is introduced, on which the enumerating polynomials or enumerating distributions are built. Within the entropy methodology three types of fractal characteristics are introduced, which characterize quasi-crystalline pentagonal Penrose tiling. The quantitative estimate for the frontal-radial fractal percolation on a Cayley’s tree graph of a Penrose tiling leading to the overdimensioned effect is calculated.     

基于快速骨架提取的三维目标识别

针对三维目标识别问题,提出了一种基于快速骨架提取的方法。根据骨架所反映的目标拓扑结构,建立了不同目标局部结构之间的对应关系;而在对应的局部曲线段上,采用基于曲线配准的方法进行匹配;以各个局部匹配的成本之和评估不同目标的相似性.这种方法在目标出现一定程度的视觉变形时仍具有较好的识别效果,同时避免了基于曲线方法的匹配目标的局部,而忽略局部之间相互的空间组织的缺点所造成的误匹配.算例结果表明这种算法对于三维目标有较好的识别效果.     

一类权重网络的加速演化模型

采用动态形成权重网络的方法，研究了在演化过程中新增边具有加速连接情况下权重网络的拓扑特性和强度分布，给出了节点强度与度的解析表达式.分析表明，加速演化的权重模型具有明显的无标度特性.再者，只要权重网络的边权重服从某一概率分布，则在演化过程中强度择优连接与度择优连接对于网络的度分布没有影响，且与具体的概率分布无关.     

基于快速骨架提取的三维目标识别(英文)

针对三维目标识别问题,提出了一种基于快速骨架提取的方法。根据骨架所反映的目标拓扑结构,建立了不同目标局部结构之间的对应关系;而在对应的局部曲线段上,采用基于曲线配准的方法进行匹配;以各个局部匹配的成本之和评估不同目标的相似性.这种方法在目标出现一定程度的视觉变形时仍具有较好的识别效果,同时避免了基于曲线方法的匹配目标的局部,而忽略局部之间相互的空间组织的缺点所造成的误匹配.算例结果表明这种算法对于三维目标有较好的识别效果.     

交通流驱动的含权网络

文章对含权复杂网络研究的最近进展给予了评述，特别报道了文章作者最近提出的一个交通流驱动的含权技术网络模型。这一模型能够同时给出网络连接度分布的幂函数律、网络强度分布的幂函数律、网络权重分布的幂函数律，以及高聚集性和非相称混合性等五大特征，因此成功地刻画了真实技术网络的无尺度性质和小世界效应     

一类高聚类系数的加权无标度网络及其同步能力分析

针对真实世界中大规模网络都具有明显聚类效应的特点, 提出一类具有高聚类系数的加权无标度网络演化模型, 该模型同时考虑了优先连接、三角结构、随机连接和社团结构等四种演化机制. 在模型演化规则中, 以概率p增加单个节点, 以概率1–p增加一个社团. 与以往研究的不同在于新边的建立, 以概率φ在旧节点之间进行三角连接, 以概率1–φ进行随机连接. 仿真分析表明, 所提出的网络度、强度和权值分布都是服从幂律分布的形式, 且具有高聚类系数的特性, 聚类系数的提高与社团结构和随机连接机制有直接的关系. 最后通过数值仿真分析了网络演化机制对同步动态特性的影响, 数值仿真结果表明, 网络的平均聚类系数越小, 网络的同步能力越强. 关键词： 无标度网络 加权网络 聚类系数 同步能力     

交通流驱动的含权网络

文章对含权复杂网络研究的最近进展给予了评述,特别报道了文章作者最近提出的一个交通流驱动的含权技术网络模型.这一模型能够同时给出网络连接度分布的幂函数律、网络强度分布的幂函数律、网络权重分布的幂函数律,以及高聚集性和非相称混合性等五大特征,因此成功地刻画了真实技术网络的无尺度性质和小世界效应.     

Stability of weighted spectral distribution in a pseudo tree-like network model

The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution(i.e., a spectral graph feature) as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.     

Order-disorder phase transition in a cliquey social network

We investigate the network model of community by Watts, Dodds and Newman (D.J. Watts et al., Science 296, 1302 (2002)) as a hierarchy of groups, each of 5 individuals. A homophily parameter α controls the probability proportional to exp (-αx) of selection of neighbours against distance x. The network nodes are endowed with spin-like variables s_i = ± 1, with Ising interaction J > 0. The Glauber dynamics is used to investigate the order-disorder transition. The transition temperature T_c is close to 3.8 for α < 0.0 and it falls down to zero above this value. The result provides a mathematical illustration of the social ability to a collective action via weak ties, as discussed by Granovetter in 1973.     

Class formation in a social network with asset exchange

We study two kinds of economic exchanges, additive and multiplicative, in a system of N agents. The work is divided into two parts. In the first one, the agents are free to interact with each other. The system evolves to a Boltzmann-Gibbs distribution with additive exchange and condenses with a multiplicative one. If bankruptcy is introduced, both types of exchange lead to condensation. Condensation times have been studied. In the second part, the agents are placed in a social network. We analyze the behavior of wealth distributions in time, and the formation of economic classes is observed for certain values of network connectivity.     

基于信息熵的社交网络观点演化模型

随着网络服务的发展,社交网络逐渐成为信息传播的新媒介.因此,研究网络舆情演化具有重要意义和实用价值.为了更好地研究网络舆论,在信息熵的基础上,提出了一个社交网络观点演化模型.此模型存在以下两个特点:一是可以反映个体面对正负两种观点趋向做出抉择时的心理过程;二是可以反映个体形成新观点时主观因素和客观因素的影响.在仿真实验中,讨论了舆论环境对个体观点演化的影响,初始观点和自信度对观点演化的影响,以及意见领袖对群体观点演化的影响.实验结果表明,该模型可以反映真实社交网络中个体的心理学特征,比如个体的观点形成会受到舆论环境的影响,自信的个体不愿意接受他人的观点,当意见领袖存在时群体的观点会受到影响等.