具有双峰特性的双层超网络模型

随着社会经济的快速发展,社会成员及群体之间的关系呈现出了更复杂、更多元化的特点.超网络作为一种描述复杂多元关系的网络,已在不同领域中得到了广泛的应用.服从泊松度分布的随机网络是研究复杂网络的开创性模型之一,而在现有的超网络研究中,基于ER随机图的超网络模型尚属空白.本文首先在基于超图的超网络结构中引入ER随机图理论,提出了一种ER随机超网络模型,对超网络中的节点超度分布进行了理论分析,并通过计算机仿真了在不同超边连接概率条件下的节点超度分布情况,结果表明节点超度分布服从泊松分布,符合随机网络特征并且与理论推导相一致.进一步,为更准确有效地描述现实生活中的多层、异质关系,本文构建了节点超度分布具有双峰特性,层间采用随机方式连接,层内分别为ER-ER,BA-BA和BA-ER三种不同类型的双层超网络模型,理论分析得到了三种双层超网络节点超度分布的解析表达式,三种双层超网络在仿真实验中的节点超度分布均具有双峰特性.     

一种信息传播促进网络增长的网络演化模型

为了研究信息传播过程对复杂网络结构演化的影响,提出了一种信息传播促进网络增长的网络演化模型,模型包括信息传播促进网内增边、新节点通过局域世界建立第一条边和信息传播促进新节点连边三个阶段,通过多次自回避随机游走模拟信息传播过程,节点根据路径节点的节点度和距离与其选择性建立连接。理论分析和仿真实验表明,模型不仅具有小世界和无标度特性,而且不同参数下具有漂移幂律分布、广延指数分布等分布特性,呈现小变量饱和、指数截断等非幂律现象,同时,模型可在不改变度分布的情况下调节集聚系数,并能够产生从同配到异配具有不同匹配模式的网络.     

基于超图的超网络相继故障分析

分析了快递超网络和电子元件超网络的相继故障扩散方式, 结合超图理论提出了2-section 图分析法和线图分析法, 并仿真分析了无标度超网络耦合映像格子的相继故障进程. 结果表明: 无标度超网络对外部攻击表现出了既鲁棒又脆弱的特性. 针对相继故障的不同扩散方式, 无标度超网络的相继故障行为表现出不同的特点. 超网络的相继故障行为和超网络的超度以及超边度分布有密切的联系, 也和超网络中超边的个数有关. 通过和同规模的Barabasi-Albert (BA)无标度网络对比, 在同一种攻击方式下同规模的无标度超网络都比BA 无标度网络表现出了更强的鲁棒性. 另外, 基于超边扩散的相继故障进程比基于节点扩散的相继故障进程更加缓慢.     

基于复杂网络理论的微博用户关系网络演化模型研究

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.     

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     

一种基于随机行走和策略连接的网络演化模型

本文提出了一个基于随机行走和策略选择的复杂网络局域演化模型RAPA. 新节点加入系统不需要全局知识,而是通过随机行走构造局域世界;然后依据概率采用随机连接,"扶贫"连接或"亲富"连接策略,从局域世界中选择节点增加连接边;最终自组织演化具有幂律特点的复杂网络. 初步的解析计算和仿真实验都表明,RAPA模型不仅重现了具有小世界特性、整体上的无标度特性,还可以演化出小变量饱和以及指数截断等现象,同时也具有明显的聚类特性,并能够构造出同配或异配等不同混合模式的网络. 关键词： 复杂网络 模型 随机行走 策略连接     

能量异构的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均和如何高效获得节点和边的负载问题,提出一种局域范围内能量异构的加权无标度拓扑演化模型.通过对节点能量与负载、能耗的关系建模,建立节点能量与点权和边权的联系,进而结合点权和加权模型给出网络的演化方式,推出点权、度和边权的幂率分布规律,最终根据网络获得的点权和边权来分析负载和能耗.仿真结果表明,提出的模型不仅能够准确计算点边的负载,而且缓解了无标度网络的节点能耗不均衡问题.     

虚拟社区网络的演化过程研究

模拟了虚拟社区网络的演化过程并研究其拓扑结构.发现虚拟社区网络在演化过程中,节点的加入、边的加入、网络中度分布、节点的度与其加入网络时间的关系、平均度随时间的变化等方面与传统的无标度网络有所不符.根据国内某论坛的实际网络数据统计与分析,提出了虚拟社区网络的演化机理——虚拟社区网络构造算法.仿真结果表明,模拟以互联网论坛为代表的虚拟社区网络时,该模型能够得到与真实网络相符的特性. 关键词： 复杂网络 虚拟社区 无标度网络     

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.     

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.     

A local-world evolving hypernetwork model

Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mechanisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is γ = 2 + 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypernetwork model shares the scale-free and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.     

Evolving hypernetwork model

Complex hypernetworks are ubiquitous in real-life systems. While a substantial body of previous research has only focused on the applications of hypernetworks, relatively little work has investigated the evolving models of hypernetworks. Considering the formations of many real world networks, we propose two evolving mechanisms of the hyperedge growth and the hyperedge preferential attachment, then construct an evolving hypernetwork model. We introduce some basic topological quantities, such as a variety of degree distributions, clustering coefficients as well as average path length. We numerically investigate these quantities in the limit of large hypernetwork size and find that our hypernetwork model shares similar qualitative features with the majority of complex networks that have been previously studied, such as the scale-free property of the degree distribution and a high degree of clustering, as well as the small-world property. It is expected that our attempt in the hypernetwork model can bring the upsurge in the study of the hypernetwork model in further.     

非均齐超网络中标度律的涌现—–富者愈富导致幂律分布吗?

建立非线性择优连接非均齐超网络演化模型,研究非均齐超网络演化机制和拓扑性质.使用Poisson过程理论和连续化方法对模型进行分析,给出超网络超度的特征方程.利用超度特征方程不仅证明网络稳态平均超度分布存在,而且获得超度分布的解析表达式.分析表明这个网络具有"富者愈富"现象.仿真实验和理论分析相符合.随着网络规模的增大,这个动态演化的非均齐超网络的超度分布表现出拉直指数分布的特征,而不一定是幂律分布.结果表明"富者愈富"不一定导致幂律分布.     

一种能耗均衡的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均衡问题,通过对节点生命期建模,得出节点生命期受节点剩余能量和通信距离的影响,进而将两端节点生命期作为构建拓扑时边权重的影响因子,通过边权重控制节点权重,最终得出了一种能耗均衡的无线传感器网络加权无标度拓扑模型,并理论证明了该模型的点权、边权和节点度均服从幂律分布.实验结果表明,该模型具有无标度拓扑的强容错性,并有效的均衡了网络中的节点能耗,延长了网络的生命期.     

Cascading Failures of Complex Networks Based on Two-Step Degree

We propose a new concept, two-step degree. Defining it as the capacity of a node of complex networks, we establish a novel capacity-load model of cascading failures of complex networks where the capacity of nodes decreases during the process of cascading failures. For scale-free networks, we find that the average two-step degree increases with the increase of the heterogeneity of the degree distribution, showing that the average two- step degree can be used for measuring the heterogeneity of the degree distribution of complex networks. In addition, under the condition that the average degree of a node is given, we can design a scale-free network with the optimal robustness to random failures by maximizing the average two-step degree.     

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of São Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model.     

The physical position neighbourhood evolving network model

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.     

The emergence of scale-free networks with a seceding mechanism

In order to explore further the underlying mechanism of scale-free networks, we study stochastic secession as a mechanism for the creation of complex networks. In this evolution the network growth incorporates the addition of new nodes, the addition of new links between existing nodes, the deleting and rewiring of some existing links, and the stochastic secession of nodes. To random growing networks with preferential attachment, the model yields scale-free behavior for the degree distribution. Furthermore, we obtain an analytical expression of the power-law degree distribution with scaling exponent γ ranging from 1.1 to 9. The analytical expressions are in good agreement with the numerical simulation results.