共查询到17条相似文献,搜索用时 156 毫秒
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受某些实际网络节点数按几何级数增长现象的启发,构造了每个时间步中按当前网络规模成比例地同时加入多个节点的节点数加速增长的网络模型.研究表明,在增长率不是很大的情况下网络度分布仍然是幂律的,但在不同的增长率r下幂律指数是不同的.得到了幂律指数介于2到3之间可调的无标度网络模型,并解析地给出了幂律指数随增长率变化的函数关系.数值模拟还显示,网络的平均最短距离随r减小而簇系数随r增大.
关键词:
复杂网络
无标度网络
生长网络模型
节点数加速增长网络模型 相似文献
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本文对互联网论坛中用户的交互行为进行实证分析,建立基于隐式交互行为的虚拟社区网络,通过考察其统计特性,发现该虚拟社区网络是有向、不对称、异配的无标度网络,且具有分层性质和社团结构.用户的浏览量与其入度正相关,二者均可描述用户的影响力和受关注程度.本文从定量的角度发现,论坛中具有"贡献驱动"特性的权威用户在信息传播中的作用不容小觑;网络论坛的成功需要提高大多数惰性用户的活跃度;用户要想在虚拟社区网络中占据权威地位,需要发表高质量的帖子或形成个人品牌.
关键词:
统计物理
复杂网络
人际交互
幂率分布 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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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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为了研究人群中的一些基本的社会关系结构,如家庭、室友、同事等,对传染病传播过程的影响机制,本文建立了一个具有局部结构的增长无标度网络模型.研究表明,局部结构的引入使得该网络模型能够同时再现社会网络的两个重要特征:节点度分布的不均匀性以及节点度之间的相关性.首先,该网络的节点度和局部结构度均服从幂律分布,且度分布指数依赖于局部结构的大小.此外,局部结构的存在还导致网络节点度之间具有正相关特性,而这种正相关正是社会网络所特有的一个重要特性.接着,通过理论分析和数值模拟,我们进一步研究了该网络结构对易感者-感染
关键词:
复杂网络
无标度网络
局部结构
传染病建模 相似文献
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基于电力网络的级联故障模型 总被引:2,自引:1,他引:1
以电力系统的停电事故为例,提出一种节点具有能量耗散和扩容行为的级联故障模型,并分别在二维规则网络和无标度网络上对该系统的演化过程进行计算机模拟.结果表明,在两种不同结构的网络中系统的演化过程都出现了自组织临界现象,说明网络中节点能量的耗散及容量的扩充是导致电力系统出现自组织临界现象的重要因素.此外,还发现无标度网络中的最大级联故障规模要远大于二维规则网络中的级联故障规模. 相似文献
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Detection of community structures in the weighted complex networks is significant to understand the network structures and analysis of the network properties. We present a unique algorithm to detect overlapping communities in the weighted complex networks with considerable accuracy. For a given weighted network, all the seed communities are first extracted. Then to each seed community, more community members are absorbed using the absorbing degree function. In addition, our algorithm successfully finds common nodes between communities. The experiments using some real-world networks show that the performance of our algorithm is satisfactory. 相似文献
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In this paper, we present a new approach to extract communities in the complex networks with considerable accuracy. We introduce the core-vertex and the intimate degree between the community and its neighboring vertices. First, we find the core-vertices as the initial community. These core-vertices are then expanded using intimate degree function during extracting community structure from the given network. In addition, our algorithm successfully finds common nodes between communities. Experimental results using some real-world networks data shows that the performance of our algorithm is satisfactory. 相似文献
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Until recently the study of failure and vulnerability in complex networks focused on the role of high degree nodes, and the relationship between their removal and network connectivity. Recent evidence suggested that in some network configurations, the removal of lower degree nodes can also cause network fragmentation. We present a disassembling algorithm that identifies nodes that are core to network connectivity. The algorithm is based on network tearing in which communities are defined and used to construct a hierarchical structure. Cut-nodes, which are located at the boundaries of the communities, are the key interest. Their importance in the overall network connectivity is characterized by their participation with neighbouring communities in each level of the hierarchy. We examine the impact of these cut-nodes by studying the change in size of the giant component, local and global efficiencies, and how the algorithm can be combined with other community detection methods to reveal the finer internal structure within a community. 相似文献
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Scale-free networks are characterized by a degree distribution with power-law behavior. Although scale-free networks have been shown to arise in many areas, ranging from the World Wide Web to transportation or social networks, degree distributions of other observed networks often differ from the power-law type. Data based investigations require modifications of the typical scale-free network.We present an algorithm that generates networks in which the shape of the degree distribution is tunable by modifying the preferential attachment step of the Barabási-Albert construction algorithm. The shape of the distribution is represented by dispersion measures such as the variance and the skewness, both of which are highly correlated with the maximal degree of the network and, therefore, adequately represents the influence of superspreaders or hubs. By combining our algorithm with work of Holme and Kim, we show how to generate networks with a variety of degree distributions and clustering coefficients. 相似文献
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Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. 相似文献
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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
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Complex networks have been applied to model numerous interactive
nonlinear systems in the real world. Knowledge about network topology
is crucial to an understanding of the function, performance and
evolution of complex systems. In the last few years, many network
metrics and models have been proposed to investigate the network
topology, dynamics and evolution. Since these network metrics and
models are derived from a wide range of studies, a systematic study
is required to investigate the correlations among them. The present
paper explores the effect of degree correlation on the other network
metrics through studying an ensemble of graphs where the degree
sequence (set of degrees) is fixed. We show that to some extent, the
characteristic path length, clustering coefficient, modular extent
and robustness of networks are directly influenced by the degree
correlation. 相似文献