加权网络权重自相似评判函数及其社团结构检测

提出了权重自相似性加权网络社团结构评判函数,并基于该函数提出一种谱分析算法检测社团结构,结果表明算法能将加权网络划分为同一社团内边权值分布均匀,而社团间边权值分布随机的社团结构.通过建立具有社团结构的加权随机网络分析了该算法的准确性,与WEO和WGN算法相比,在评判权重自相似的阈值系数取较小时,该算法具有较高的准确性.对于一个具有n个节点和c个社团的加权网络,社团结构检测的复杂度为O(cn2/2).通过设置评判权重自相似的阈值系数,可检测出能反映节点联系稳定性的层化性社团结构.这与传统意义上只将加权网络划分为社团中边权值较大而社团间边权值较小的标准不同,从另一个角度更好地提取了加权网络的结构信息.     

基于联合矩阵分解的节点多属性网络社团检测

发现复杂网络中的社团结构在社会网络、生物组织网络和在线网络等复杂网络中具备十分重要的意义. 针对社交媒体网络的社团检测通常需要利用两种信息源: 网络拓扑结构特征和节点属性特征, 丰富的节点内容属性信息为社团检测的增加了灵活性和挑战. 传统方法是要么仅针对这两者信息之一进行单独挖掘, 或者将两者信息得到的社团结果进行线性叠加判决, 不能有效进行信息源的融合. 本文将节点的多维属性特征作为社团划分的一种有效协同学习项进行研究, 将两者信息源进行融合分析, 提出了一种基于联合矩阵分解的节点多属性网络社团检测算法CDJMF, 提高了社团检测的有效性和鲁棒性. 实验表明, 本文所提的方法能够有效利用节点的属性信息指导社团检测, 具备更高的社团划分质量.     

基于社团结构的负载传输优化策略研究

研究表明网络社团结构特征对负载传输有影响,明显社团结构特征会降低网络的承载能力.由于最短路由策略在选择路由时有一定的随机性,本文提出了一种基于社团结构的负载传输策略,减少最短路由经过的社团数量,从而降低社团边缘节点的介数.实验结果显示,该策略在保证最短路由小世界特性的同时,提升了网络的承载能力,社团划分得越准确传输优化策略效果越显著. 关键词： 优化路由策略 社团结构 复杂网络 负载传输     

加权复杂网络社团的评价指标及其发现算法分析

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.     

基于区块链的混合式点对点电力信息网络级联故障研究

通过对电网进行社团划分,根据电网和信息网对应分层分区建设的现状和实际耦合关系划分信息网社团。选取信息网中每个社团介数最大的节点和度数最大的节点进行全连接,构建基于区块链的混合式点对点结构的电力信息相互依存网络模型。结合区块链的共识机制分析该模型的优势,利用高度数攻击与高介数攻击这两种攻击策略研究所提出的相互依存网络的鲁棒性,并与传统的集中式控制的电力信息网和完全分散式点对点电力信息网进行对比。研究结果表明：本电力信息相互依存网络模型能够在提高网络鲁棒性的基础上,有效减少实用拜占庭容错算法的通信开销和共识时延;在混合式点对点电力信息网中,高度数攻击方式下系统表现出更强的脆弱性。     

基于簇相似度的网络社团结构探测算法

社团结构对复杂系统的结构特性和动力学特性有重要影响.提出了一个度量社团相似度的模型,称为簇相似度.该模型能够度量两个社团的相似度大小,为研究社团间的作用机制提供帮助.而且基于该模型,设计了一个社团划分算法.算法采用层次聚类的思想,每次合并两个相似度最大的社团,并通过一个评价函数选择最优社团划分.数值实验以及与CNM,GN,EigenMod等主流算法做比较,表明本算法的精度和效率都比较高,尤其对于边密度较高的网络,性能非常理想.     

复杂网络中社团结构发现的多分辨率密度模块度

现实中的许多复杂网络呈现出明显的模块性或社团性.模块度是衡量社团结构划分优劣的效益函数, 它也通常被用作社团结构探测的目标函数,但最为广泛使用的Newman-Girvan模块度却存在着分辨率限制问题,多分辨率模块度也不能克服误合并社团和误分裂社团同时存在的缺陷. 本文在网络密度的基础上提出了多分辨率的密度模块度函数, 通过实验和分析证实了该函数能够使社团结构的误划分率显著降低, 而且能够体现出网络社团结构是一个有机整体,不是各个社团的简单相加.     

两相流流型复杂网络社团结构及其统计特性

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     

一种有效提高无标度网络负载容量的管理策略

现有研究表明明显的社团结构会显著降低网络的传输性能. 本文基于网络邻接矩阵的特征谱定义了链路对网络社团特性的贡献度, 提出一种通过逻辑关闭或删除对网络社团特性贡献度大的链路以提高网络传输性能的拓扑管理策略, 即社团弱化控制策略(CWCS 策略). 在具有社团结构的无标度网络上分别进行了基于全局最短路径路由和局部路由的仿真实验, 并与关闭连接度大的节点之间链路的HDF 策略进行了比较. 仿真实验结果显示, 在全局最短路径路由策略下, CWCS策略能更有效地提高网络负载容量, 并且网络的平均传输时间增加的幅度变小. 在局部路由策略下, 当调控参数0<α<2, 对网络负载容量的提升优于HDF策略. 关键词： 复杂网络 社团特性 负载容量 拓扑管理     

基于子网划分的电网关键节点识别

结合电网拓扑结构和潮流追踪技术,提出一种基于子网划分的电网关键节点识别方法。首先,根据发电机节点的邻域信息和功率将发电机节点划分为不同的子集,然后根据电网的系数分配矩阵将负荷节点划分到为其提供最大功率的发电机节点子集中,完成子网划分。接着采用多属性决策法对每个子网的节点进行排序,进一步改进并计算每个子网的结构系数,作为衡量子网重要性的指标。根据子网重要性,从每个子网中提取特定比例的候选关键节点,对这些候选节点依据多属性决策法重新排序,得到关键节点的最终排序。以IEEE14、IEEE57和IEEE118三种节点系统为例进行分析,得到各个系统的子网划分结果和各个标准网络的重要节点排序结果。采用本文方法、PageRank法和多属性决策法分别进行关键节点排序,并对排序靠前的关键节点进行级联故障性能实验和网络效能实验。实验表明,本文算法选择的关键节点对整个网络的传播性能影响最大,优于其他两种关键节点识别方法。     

Detecting local community structures in complex networks based on local degree central nodes

Detecting local communities in real-world graphs such as large social networks, web graphs, and biological networks has received a great deal of attention because obtaining complete information from a large network is still difficult and unrealistic nowadays. In this paper, we define the term local degree central node whose degree is greater than or equal to the degree of its neighbor nodes. A new method based on the local degree central node to detect the local community is proposed. In our method, the local community is not discovered from the given starting node, but from the local degree central node that is associated with the given starting node. Experiments show that the local central nodes are key nodes of communities in complex networks and the local communities detected by our method have high accuracy. Our algorithm can discover local communities accurately for more nodes and is an effective method to explore community structures of large networks.     

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.     

一种基于效率矩阵的网络节点重要度评价算法

为了实现对网络节点重要性的有效评价,提出一种基于网络效率矩阵的节点重要度评价算法.该方法综合考虑节点的度值(局部重要度)和网络节点之间的重要性贡献(全局重要度),利用节点的度和效率矩阵表征网络节点的重要度贡献,克服重要性贡献矩阵法中节点只依赖于邻接节点的不足.考虑实际网络的稀疏性,该算法的时间复杂度为O(n²).通过算例分析验证了该算法的可行性和有效性,结果表明:该算法能够更加直观、简单有效地区分节点的重要度差异,并且对于大型复杂网络具有较理想的计算能力.     

Overlapping community detection using a generative model for networks

Detecting overlapping communities is a challenging task in analyzing networks, where nodes may belong to more than one community. Many present methods optimize quality functions to extract the communities from a network. In this paper, we present a probabilistic method for detecting overlapping communities using a generative model. The model describes the probability of generating a network with the model parameters, which reflect the communities in the network. The community memberships of each node are determined based on a probabilistic approach using those model parameters, whose values can be obtained by fitting the model to the network. This method has the advantage that the node participation degrees in each community are also computed. The proposed method is compared with some other community detection methods on both synthetic networks and real-world networks. The experiments show that this method is efficient at detecting overlapping communities and can provide better performance on the networks where a majority of nodes belong to more than one community.     

A thermal flux-diffusing model for complex networks and its applications in community structure detection

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.     

Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.     

An Efficient Partition-Based Approach to Identify and Scatter Multiple Relevant Spreaders in Complex Networks

One of the main problems in graph analysis is the correct identification of relevant nodes for spreading processes. Spreaders are crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases, rumors, and more. Correct identification of spreaders in graph analysis is a relevant task to optimally use the network structure and ensure a more efficient flow of information. Additionally, network topology has proven to play a relevant role in the spreading processes. In this sense, more of the existing methods based on local, global, or hybrid centrality measures only select relevant nodes based on their ranking values, but they do not intentionally focus on their distribution on the graph. In this paper, we propose a simple yet effective method that takes advantage of the underlying graph topology to guarantee that the selected nodes are not only relevant but also well-scattered. Our proposal also suggests how to define the number of spreaders to select. The approach is composed of two phases: first, graph partitioning; and second, identification and distribution of relevant nodes. We have tested our approach by applying the SIR spreading model over nine real complex networks. The experimental results showed more influential and scattered values for the set of relevant nodes identified by our approach than several reference algorithms, including degree, closeness, Betweenness, VoteRank, HybridRank, and IKS. The results further showed an improvement in the propagation influence value when combining our distribution strategy with classical metrics, such as degree, outperforming computationally more complex strategies. Moreover, our proposal shows a good computational complexity and can be applied to large-scale networks.     

Detecting communities by the core-vertex and intimate degree in complex networks

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.     

Detecting overlapping communities of weighted networks via a local algorithm

Identification of communities is significant in understanding the structures and functions of networks. Since some nodes naturally belong to several communities, the study of overlapping communities has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. In this paper, an overlapping communities detecting algorithm is proposed whose main strategies are finding an initial partial community from a node with maximal node strength and adding tight nodes to expand the partial community. Seven real-world complex networks and one synthetic network are used to evaluate the algorithm. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in weighted networks.     

Detecting overlapping communities by seed community in weighted complex networks

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.