共查询到16条相似文献,搜索用时 62 毫秒
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节点的聚集现象是复杂网络的重要特性.以往研究主要发现无权复杂网络中的社团,较少涉及加权网络的社团发现.由于加权网络的复杂性远高于无权网络,一般认为加权网络的社团发现是一个较难的问题.本文基于统一的数据基础,从社团评价指标的有效性和现有算法的效果两个角度开展研究.首先,总结了加权网络三种常见的社团评估指标,并在社团大小、密度和局域特点均不同的模拟数据集上分析指标的有效性;其次,针对5个数据集,分析现有的3种加权复杂网络社团发现算法的效果.研究表明:上述指标无论在评价最基本的社团结构,还是在分析结构复杂的社团时都有较大缺欠;现有的加权网络社团发现算法的泛化能力不强. 相似文献
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利用气液两相流电导波动信号构建了流型复杂网络. 基于K均值聚类的社团探寻算法对该网络的社团结构进行了分析,发现该网络存在分别对应于泡状流、段塞流及混状流的三个社团,并且两个社团间联系紧密的点分别对应于相应的过渡流型. 基于复杂网络理论从全新的角度探讨了两相流流型复杂网络社团结构及统计特性问题,并取得了满意的流型识别效果,与此同时,在对该网络特性进一步分析的基础上,发现了对两相流流动参数变化敏感的相关复杂网络统计量,为更好地理解两相流流型动力学特性提供了参考.
关键词:
两相流流型
复杂网络
社团探寻算法
网络统计特性 相似文献
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本文运用复杂网络理论, 对我国北京、上海、广州和深圳等城市的地铁网络进行了实证研究. 分别研究了地铁网络的度分布、聚类系数和平均路径长度. 研究表明, 该网络具有高的聚类系数和短的平均路径长度, 显示小世界网络的特征, 其度分布并不严格服从幂律分布或指数分布, 而是呈多段的分布, 显示层次网络的特征. 此外, 它还具有重叠的社团结构特征. 基于实证研究的结果, 提出一种基于社团结构的交通网络模型, 并对该模型进行了模拟分析, 模拟结果表明, 该模型的模拟结果与实证研究结果相符. 此外, 该模型还能解释其他类型的复杂网络(如城市公共汽车交通网络)的网络特性.
关键词:
复杂网络
地铁网络
小世界
社团 相似文献
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对网络中节点的传播影响力进行评估具有十分重要的意义, 有助于促进有益或抑制有害信息的传播. 目前, 多种中心性指标可用于对节点的传播影响力进行评估, 然而它们一般只有当传播率处于特定范围时才能取得理想的结果. 例如, 度值中心性指标在传播率较小时较为合适, 而半局部中心性和接近中心性指标则适用于稍大一些的传播率. 为了解决各种评估指标对传播率敏感的问题, 提出了一种基于扩展度的传播影响力评估算法. 算法利用邻居节点度值叠加的方式对节点度的覆盖范围进行了扩展, 使不同的扩展层次对应于不同的传播率, 并通过抽样测试确定了适合于特定传播率的层次数. 真实和模拟数据集上的实验结果表明, 通过扩展度算法得到的扩展度指标能在不同传播率下对节点的传播影响力进行有效评估, 其准确性能够达到或优于利用其他中心性指标进行评估的结果. 相似文献
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针对气液两相流流动特性,利用有限元分析方法设计变曲率对壁式电导传感器.采用设计加工的传感器在多相流装置上进行气液两相流动态实验,并测得多组对应于不同流型的电导波动信号. 基于测量数据,采用多元时间序列复杂网络构建算法构建对应于不同流型的复杂网络.在此基础上, 对网络的社团特性进行了分析, 研究发现,不同的社团结构对应于不同的流型,而社团内部网络特征可有效刻画不同流型内在动力学特性.多元时间序列复杂网络分析可为两相流流型演化动力学特性研究及流型识别提供新理论、开拓新途经. 相似文献
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A thermal flux-diffusing model for complex networks and its applications in community structure detection 下载免费PDF全文
We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally, several real-world networks are investigated. 相似文献
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In this paper, we propose a simple model that can generate
small-world network with community structure. The network is
introduced as a tunable community organization with parameter r,
which is directly measured by the ratio of inter- to intra-community
connectivity, and a smaller r corresponds to a stronger community
structure. The structure properties, including the degree
distribution, clustering, the communication efficiency and
modularity are also analysed for the network. In addition, by using
the Kuramoto model, we investigated the phase synchronization on
this network, and found that increasing the fuzziness of community
structure will markedly enhance the network synchronizability;
however, in an abnormal region (r ≤ 0.001), the network has even
worse synchronizability than the case of isolated communities (r =
0). Furthermore, this network exhibits a remarkable
synchronization behaviour in topological scales: the oscillators of
high densely interconnected communities synchronize more easily, and
more rapidly than the whole network. 相似文献
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模块性(modularity)是度量网络社区结构(community structure)的主要参数.探讨了Watts和Strogatz的小世界网络(简称W-S模型)以及Barabàsi 等的B-A无标度网络(简称B-A模型)两类典型复杂网络模块性特点.结果显示,网络模块性受到网络连接稀疏的影响,W-S模型具有显著的社区结构,而B-A模型的社区结构特征不明显.因此,应用中应该分别讨论网络的小世界现象和无标度特性.社区结构不同于小世界现象和无标度特性,并可以利用模块性区别网络类型,因此网络复杂性指标应该包括
关键词:
模块性
社区结构
小世界网络
无标度网络 相似文献
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Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within the physics literature. Here we investigate the scaling of density in complex networks under two classical box-covering renormalizations–network coarse-graining–and also different community-based renormalizations. The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin. The results thus advance a recent discovery of a universal scaling of density among different real-world networks [P.J. Laurienti, K.E. Joyce, Q.K. Telesford, J.H. Burdette, S. Hayasaka, Universal fractal scaling of self-organized networks, Physica A 390 (20) (2011) 3608–3613] and imply an existence of a scale-free density also within–among different self-similar scales of–complex real-world networks. The latter further improves the comprehension of self-similar structure in large real-world networks with several possible applications. 相似文献
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The detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. Based on node similarity, a fast and efficient method for detecting community structure is proposed, which discovers the community structure by iteratively incorporating the community containing a node with the communities that contain the nodes with maximum similarity to this node to form a new community. The presented method has low computational complexity because of requiring only the local information of the network, and it does not need any prior knowledge about the communities and its detection results are robust on the selection of the initial node. Some real-world and computer-generated networks are used to evaluate the performance of the presented method. The simulation results demonstrate that this method is efficient to detect community structure in complex networks, and the ZLZ metrics used in the proposed method is the most suitable one among local indices in community detection. 相似文献
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Discovering a community structure is fundamental for uncovering the links between structure and function in complex networks. In this paper, we discuss an equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density. Based on this equivalence, we develop a new algorithm, named the so-called SNMF-SS, by combining SNMF and a semi-supervised clustering approach. Previous NMF-based algorithms often suffer from the restriction of measuring network topology from only one perspective, but our algorithm uses a semi-supervised mechanism to get rid of the restriction. The algorithm is illustrated and compared with spectral clustering and NMF by using artificial examples and other classic real world networks. Experimental results show the significance of the proposed approach, particularly, in the cases when community structure is obscure. 相似文献