共查询到16条相似文献,搜索用时 206 毫秒
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本文构建超网络和复杂网络中统一演化模型,研究超网络无标度特性演化机理和拓扑性质.利用Poisson过程理论和连续化方法对模型进行分析,获得网络稳态平均超度分布的解析表达式.仿真实验和理论分析相符合.结果表明:随着网络规模的增大,这个动态演化网络的超度分布遵循无标度的特性.它不仅将每次增加一个新节点与若干个老节点围成一条超边的超网络模型和每次增加若干个新节点与一个老节点围成一条超边的超网络模型统一在一个模型中,而且将复杂网络中著名的无标度模型也作为我们模型的特例. 相似文献
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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. 相似文献
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中国铁路客运系统可以采用两种不同的网络构建方式来描述. 一种是以铁路的站点作为“节点”,并以轨道作为“边”,这样生成的网络称为铁路地理网. 统计显示该网络的平均群聚系数〈C〉近似为零,故该网络为树状网络. 另一种是以站点作为“节点”,任意两个站点间只要有同一列车在这两个站点停靠,就可以认为这两个站点间有连线,这样生成的网络称为车流网. 统计显示该网络有较大的平均群聚系数和较小的平均网络距离〈d〉,而且该网络节点的度分布基本上服从无标度幂律分布,故车流网为具有无标度性质的小世界网络.
关键词:
铁路地理网
车流网
小世界
无标度分布 相似文献
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微博给人们提供便利的同时也产生了较大的负面影响.为获取微博谣言的传播规律,进而采取有效措施防控其传播,本文基于复杂网络理论研究微博用户关系网络的内部特征,提出一种微博用户关系网络演化模型,借助于平均场理论,分析该演化模型的拓扑统计特性,以及谣言在该演化模型上的传播动力学行为.理论分析和仿真实验表明,由该模型演化生成的微博用户关系网络具有无标度特性.度分布指数不仅与反向连接概率有关,而且还取决于节点的吸引度分布.研究还发现,与指数分布和均匀分布相比,当节点吸引度满足幂律分布时,稳态时的谣言传播程度较大.此外,随着反向连接概率或节点初始连边数量的增加,谣言爆发的概率以及网络中最终接受谣言的节点数量都会明显增大. 相似文献
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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 相似文献
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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 相似文献
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In software engineering, class diagrams are often used to describe the system's
class structures in Unified Modelling Language (UML). A class diagram, as a graph,
is a collection of static declarative model elements, such as classes, interfaces,
and the relationships of their connections with each other. In this paper, class
graphs are examined within several Java software systems provided by Sun and IBM,
and some new features are found. For a large-scale Java software system, its
in-degree distribution tends to an exponential distribution, while its out-degree
and degree distributions reveal the power-law behaviour. And then a directed
preferential-random model is established to describe the corresponding degree
distribution features and evolve large-scale Java software systems. 相似文献
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In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner-module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module. 相似文献
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The ever-increasing knowledge of the structure of various real-world networks has uncovered their complex multi-mechanism-governed evolution processes. Therefore, a better understanding of the structure and evolution of these networked complex systems requires us to describe such processes in a more detailed and realistic manner. In this paper, we introduce a new type of network growth rule which comprises addition and deletion of nodes, and propose an evolving network model to investigate the effect of node deleting on network structure. It is found that, with the introduction of node deleting, network structure is significantly transformed. In particular, degree distribution of the network undergoes a transition from scale-free to exponential forms as the intensity of node deleting increases. At the same time, nontrivial disassortative degree correlation develops spontaneously as a natural result of network evolution in the model. We also demonstrate that node deleting introduced in the model does not destroy the connectedness of a growing network so long as the increasing rate of edges is not excessively small. In addition, it is found that node deleting will weaken but not eliminate the small-world effect of a growing network, and generally it will decrease the clustering coefficient in a network. 相似文献
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Many social, technological, biological and economical systems are properly described by evolved network models. In this paper, a new evolving network model with the concept of physical position neighbourhood connectivity is proposed and studied. This concept exists in many real complex networks such as communication networks. The simulation results for network parameters such as the first nonzero eigenvalue and maximal eigenvalue of the graph Laplacian, clustering coefficients, average distances and degree distributions for different evolving parameters of this model are presented. The dynamical behaviour of each node on the consensus problem is also studied. It is found that the degree distribution of this new model represents a transition between power-law and exponential scaling, while the Barábasi-Albert scale-free model is only one of its special (limiting) cases. It is also found that the time to reach a consensus becomes shorter sharply with increasing of neighbourhood scale of the nodes. 相似文献
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We propose and study an evolving network model with both preferential and random attachments of new links, incorporating the
addition of new nodes, new links, and the removal of links. We first show that the degree evolution of a node follows a nonhomogeneous
Markov chain. Based on the concept of Markov chain, we provide the exact solution of the degree distribution of this model
and show that the model can generate scale-free evolving network. 相似文献
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We consider a random network evolving in continuous time in which new nodes are born and old may die, and where undirected
edges between nodes are created randomly and may also disappear. The node population is Markovian and so is the creation and
deletion of edges, given the node population. Each node is equipped with a random social index and the intensity at which
a node creates new edges is proportional to the social index, and the neighbour is either chosen uniformly or proportional
to its social index in a modification of the model. We derive properties of the network as time and the node population tends
to infinity. In particular, the degree-distribution is shown to be a mixed Poisson distribution which may exhibit a heavy
tail (e.g. power-law) if the social index distribution has a heavy tail. The limiting results are verified by means of simulations,
and the model is fitted to a network of sexual contacts. 相似文献