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1.
We propose a model for the growth of weighted networks that couples the establishment of new edges and vertices and the weights' dynamical evolution. The model is based on a simple weight-driven dynamics and generates networks exhibiting the statistical properties observed in several real-world systems. In particular, the model yields a nontrivial time evolution of vertices' properties and scale-free behavior for the weight, strength, and degree distributions. 相似文献
2.
We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks.In this model,we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it.The network evolves according to a vertex strength preferential selection mechanism.During the evolution process,the network always holds its total number of vertices and its total number of single-edges constantly.We show analytically and numerically that a network will form steady scale-free distributions with our model.The results show that a weighted non-growth random network can evolve into scale-free state.It is interesting that the network also obtains the character of an exponential edge weight distribution.Namely,coexistence of scale-free distribution and exponential distribution emerges. 相似文献
3.
We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges. 相似文献
4.
L. Tian D.-N. Shi 《The European Physical Journal B - Condensed Matter and Complex Systems》2007,56(2):167-171
In this paper, we study a rank-based model for weighted network. The evolution rule of the network is based on the ranking
of node strength, which couples the topological growth and the weight dynamics. Analytically and by simulations, we demonstrate
that the generated networks recover the scale-free distributions of degree and strength in the whole region of the growth
dynamics parameter (α>0). Moreover, this network evolution mechanism can also produce scale-free property of weight, which
adds deeper comprehension of the networks growth in the presence of incomplete information. We also characterize the clustering
and correlation properties of this class of networks. It is showed that at α=1 a structural phase transition occurs, and for
α>1 the generated network simultaneously exhibits hierarchical organization and disassortative degree correlation, which is
consistent with a wide range of biological networks. 相似文献
5.
Many real systems possess accelerating statistics where the total number of edges grows faster than the network size. In this paper, we propose a simple weighted network model with accelerating growth. We derive analytical expressions for the evolutions and distributions for strength, degree, and weight, which are relevant to accelerating growth. We also find that accelerating growth determines the clustering coefficient of the networks. Interestingly, the distributions for strength, degree, and weight display a transition from scale-free to exponential form when the parameter with respect to accelerating growth increases from a small to large value. All the theoretical predictions are successfully contrasted with numerical simulations. 相似文献
6.
为分析公交复杂网络的拓扑性质, 本文以北京市为例, 选取截止到2010年7月的北京全市(14区、2县)的1165条公交线路和9618个公交站点为样本数据, 运用复杂网络理论构建起基于邻接站点的有向加权复杂网络模型. 该方法以公交站点作为节点, 相邻站点之间的公交线路作为边, 使得网络既具有复杂网络的拓扑性质同时节点(站点)又具有明确的地理坐标. 对网络中节点度、点强度、强度分布、平均最短路径、聚类系数等性质的分析显示, 公交复杂网络的度和点强度分布极为不均, 网络中前5%和前10%节点的累计强度分布分别达到22.43%和43.02%; 点强度与排列序数、累积强度分布都服从幂律分布, 具有无标度和小世界的网络特点, 少数关键节点在网络中发挥着重要的连接作用. 为分析复杂网络中的关键节点, 本文通过承载压力分析和基于"掠夺" 的区域中心节点提取两种方法, 得到了公交复杂网络中两类不同表现的关键节点. 这些规律也为优化城市公交网络及交通规划发展提供了新的参考建议. 相似文献
7.
An improved weighted scale-free network, which has two evolution
mechanisms: topological growth and strength dynamics, has been
introduced. The topology structure of the model will be explored
in details in this work. The evolution driven mechanism of
Olami-Feder-Christensen (OFC) model is added to our model to study
the self-organized criticality and the dynamical behavior. We also
consider attack mechanism and the study of the model with attack
is also investigated in this paper. We find there are differences
between the model with attack and without attack. 相似文献
8.
We propose a deterministic weighted scale-free small-world model for considering pseudofractal web with the co-evolution of topology and weight. Considering the fluctuations in traffic flow constitute a main reason for congestion of packet delivery and poor performance of communication networks, we suggest a recursive algorithm to generate the network, which restricts the traffic fluctuations on it effectively during the evolutionary process. We provide a relatively complete view of topological structure and weight dynamics characteristics of the networks such as weight and strength distribution, degree correlations, average clustering coefficient and degree-cluster correlations as well as the diameter. 相似文献
9.
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 相似文献
10.
Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks (BTNs) evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper. 相似文献
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13.
Zhongzhi Zhang Shuigeng Zhou Lichao Chen Jihong Guan Lujun Fang Yichao Zhang 《The European Physical Journal B - Condensed Matter and Complex Systems》2007,59(1):99-107
We propose a geometric growth model for weighted
scale-free networks, which is controlled by two tunable parameters.
We derive exactly the main characteristics of the networks, which
are partially determined by the parameters. Analytical results
indicate that the resulting networks have power-law distributions of
degree, strength, weight and betweenness, a scale-free behavior for
degree correlations, logarithmic small average path length and
diameter with network size. The obtained properties are in agreement
with empirical data observed in many real-life networks, which shows
that the presented model may provide valuable insight into the real
systems. 相似文献
14.
We present a weighted scale-free network model, in which the power-law exponents can be controlled by the model parameters. The network is generated through the weight-driven preferential attachment of new nodes to existing nodes and the growth of the weights of existing links. The simplicity of the model enables us to derive analytically the various statistical properties, such as the distributions of degree, strength, and weight, the degree-strength and degree-weight relationship, and the dependencies of these power-law exponents on the model parameters. Finally, we demonstrate that networks of words, coauthorship of researchers, and collaboration of actor/actresses are quantitatively well described by this model. 相似文献
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16.
In present paper, we propose a highly clustered weighted network model that incorporates the addition of a new node with some links, new links between existing nodes and the edge's weight dynamical evolution based on weight-dependent walks at each time step. The analytical approach and numerical simulation show that the system grows into a weighted network with the power-law distributions of strength, weight and degree. The weight-dependent walk length l will not influence the strength distribution, but the clustering coefficient of the network is sensitive to l. Particularly, the clustering coefficient is especially high and almost independent of the network size when l=2. 相似文献
17.
LI Ke-Ping 《理论物理通讯》2006,46(8)
In this work, we propose a new model of evolution networks, which is based on the evolution of the traffic flow. In our method, the network growth does not take into account preferential attachment, and the attachment of new node is independent of the degree of nodes. Our aim is that employing the theory of evolution network, we give a further understanding about the dynamical evolution of the traffic flow. We investigate the probability distributions and scaling properties of the proposed model. The simulation results indicate that in the proposed model, the distribution of the output connections can be well described by scale-free distribution. Moreover, the distribution of the connections is largely related to the traffic flow states, such as the exponential distribution (i.e., the scale-free distribution) and random distribution etc. 相似文献
18.
LI Ke-Ping 《理论物理通讯》2006,46(2):374-380
In this work, we propose a new model of evolution networks, which is based on the evolution of the traffic flow. In our method, the network growth does not take into account preferential attachment, and the attachment of new node is independent of the degree of nodes. Our aim is that employing the theory of evolution network, we give a further understanding about the dynamical evolution of the traffic flow. We investigate the probability distributions and scaling properties of the proposed model The simulation results indicate that in the proposed model, the distribution of the output connections can be well described by scale-free distribution. Moreover, the distribution of the connections is largely related to the traffic flow states, such as the exponential distribution (i.e., the scale-free distribution) and random distribution etc. 相似文献
19.
We investigate the evolutionary prisoner's dilemma on a weighted scale-free network where the diversity of teaching ability is introduced to the network in the form of weight. Though the diversity of teaching ability is not a sufficient condition for the enhancement of cooperation, we find that the degree-dependent teaching ability plays an active role in the evolution of cooperation. A new phenomenon is found when the degree-dependent teaching ability is used: the distribution of the cooperator frequency displays a two-peak structure for a certain parameter range. We also investigate the effects of the degree-degree correlation of the network on the evolution of cooperation in the presence of the diversity of the teaching ability. 相似文献
20.
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 相似文献