An efficient community detection method based on rank centrality

Community detection is a very important problem in social network analysis. Classical clustering approach, K$K$-means, has been shown to be very efficient to detect communities in networks. However, K$K$-means is quite sensitive to the initial centroids or seeds, especially when it is used to detect communities. To solve this problem, in this study, we propose an efficient algorithm K$K$-rank, which selects the top-K$K$ nodes with the highest rank centrality as the initial seeds, and updates these seeds by using an iterative technique like K$K$-means. Then we extend K$K$-rank to partition directed, weighted networks, and to detect overlapping communities. The empirical study on synthetic and real networks show that K$K$-rank is robust and better than the state-of-the-art algorithms including K$K$-means, BGLL, LPA, infomap and OSLOM.     

Routing in scale-free networks based on expanding betweenness centrality

In this paper, an improved routing strategy is proposed for enhancing the traffic capacity of scale-free networks. Instead of using the information of degree and betweenness centrality, the new algorithm is derived on the basis of the expanding betweenness centrality of nodes, which gives an estimate of the traffic handled by the vertex for a certain route set. Since the nodes with large betweenness centrality are more susceptible to traffic congestion, the traffic can be improved by redistributing traffic loads from nodes with large betweenness centrality to nodes with small betweenness centrality in the process of computing the collective routing table. Comparing with results of previous routing strategies, it is shown that the present improved routing performs more effectively.     

微博双向“关注”网络节点中心性及传播 影响力的分析

根据新浪微博的实际数据, 建立了两个基于双向“关注”的用户关系网络, 通过分析网络拓扑统计特征, 发现二者均具有小世界、无标度特征. 通过对节点度、紧密度、介数和k-core 四个网络中心性指标进行实证分析, 发现节点度服从分段幂率分布; 介数相比其他中心性指标差异性最为显著; 两个网络均具有明显的层次性, 但不是所有度值大的节点核数也大; 全局范围内各中心性指标之间存在着较强的相关性, 但在度值较大的节点群这种相关性明显减弱. 此外, 借助基于传染病动力学的SIR信息传播模型来分析四种指标在刻画节点传播能力方面的差异性, 仿真结果表明, 选择具有不同中心性指标的初始传播节点, 对信息传播速度和范围均具有不同影响; 紧密度和k-core较其他指标可以更加准确地描述节点在信息传播中所处的网络核心位置, 这有助于识别信息传播拓扑网络中的关键节点.     

复杂网络中心性对灾害蔓延的影响

基于一个普适性的灾害蔓延动力学模型,在三种网络拓扑结构(随机网、小世界网和无标度网)下,仿真分析了网络中心性对灾害蔓延速度和扩散趋势的影响.通过改变初始蔓延条件来分析网络初始状态对蔓延效率的影响,并着重讨论了在四种初始崩溃节点选取策略下灾害蔓延最终状态的差异.结果表明:对于四种攻击策略,网络最终状态有着明显的差异,网络对随机攻击具有较强的抵御能力,而对于目标,攻击却显示较强的脆弱性,或许,三种网络表现出不同的脆弱程度.最后,在一个实际网络上对理论分析结果进行了验证.     

Edge intensity-based community measurement in complex networks

Community is the dominant structure of complex networks. In recent years, community detection has become a heavily researched issue in network science, and many algorithms have been proposed to solve it. However, how to evaluate these algorithms and measure the strength of community structures is still an open problem. The modularity, as well as many of its variants, is widely used for this purpose, and maximizing such metrics is also a main approach to uncover communities, but this technique has a resolution limit problem in some cases, which means larger structures are favored over smaller ones. In this paper, we define the edge intensity to measure local density of network and propose an intensity-based measurement to support community evaluation; with an additional constraint the proposed measurement would also support multiresolution investigation of the networks. Experimental results on synthetic and real networks illustrate that the maximization of the new metric further reduces the resolution limit problem, and the maximization of the restricted intensity-based measurement provides multiresolution details of the investigated networks.     

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks, solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.     

网络集聚性对节点中心性指标的准确性影响

节点中心性指标是从特定角度对网络某一方面的结构特点进行刻画的度量指标, 因此网络拓扑结构的改变会对节点中心性指标的准确性产生重要影响. 本文利用Holme-Kim模型构建可变集聚系数的无标度网络, 然后采用Susceptible-Infective-Removal模型进行传播影响力的仿真实验, 接着分析了节点中心性指标在不同集聚系数的无标度网络中的准确性. 结果表明, 度中心性和介数中心性的准确性在低集聚系数的网络中表现更好, 特征向量中心性则在高集聚类网络中更准确, 而紧密度中心性的准确性受网络集聚系数的变化影响较小. 因此当网络的集聚系数较低时, 可选择度或者介数作为中心性指标进行网络节点影响力评价; 反之则选择紧密度指标或特征向量指标较好, 尤其当网络的集聚系数接近0.6时特征向量的准确性可以高达到0.85, 是度量小规模网络的较优选择. 另一方面, 传播过程的感染率越高, 度指标和介数指标越可靠, 紧密度和特征向量则相反. 最后Autonomous System实证网络的断边重连实验, 进一步验证了网络集聚性的改变会对节点中心性指标的准确性产生重要影响.     

An effusion-evaporation model for image edge detection

A novel quasi-physical edge detection model is presented. The model, referred to as the effusion-evaporation model (EEM), is inspired by the natural phenomenon that the water effusing from the ground evaporates in the sunshine and leaves a wire like water stain on the ground surface, which reflects the physiognomy of the terrain. Based on the simulation of water effusing and evaporating, an EEM regards the complement of gradient magnitude image as a three-dimensional terrain, and the concave regions, which contain the residual water in the evolution final state, are used to determine the edges. Subjective and objective comparisons are performed on the proposed algorithm and two conventional edge detectors, namely Canny and LoG. The comparison results show that the proposed method outperforms Canny and LoG detectors for the real images and the standard test images with Gaussian noise.     

A vibrational approach to node centrality and vulnerability in complex networks

We propose a new measure of vulnerability of a node in a complex network. The measure is based on the analogy in which the nodes of the network are represented by balls and the links are identified with springs. We define the measure as the node displacement, or the amplitude of vibration of each node, under fluctuation due to the thermal bath in which the network is supposed to be submerged. We prove exact relations among the thus defined node displacement, the information centrality and the Kirchhoff index. The relation between the first two suggests that the node displacement has a better resolution of the vulnerability than the information centrality, because the latter is the sum of the local node displacement and the node displacement averaged over the entire network.     

节点中心性对复杂网络传播模式的影响分析

在众多的重要节点评估方法研究中,具有较高中心性的节点一直是关注的焦点,许多传播行为的研究也主要围绕高中心性节点展开,因此在一定程度上忽略了低中心性节点对传播行为的影响.本文从传播异构性角度,通过初始感染最大中心性节点和最小中心性节点揭示网络结构异构性对信息传播的影响.实验结果表明,传播过程中存在"链型"和"扇型"两种传播模式,在初始感染比例不断提升的情况下,两种传播模式的相互转换引发传播速率的变化,进一步促使非线性传播规模交叉现象的产生.这一现象说明,在宏观的信息传播过程中,最小中心性节点的影响力不容忽视,尤其在初始感染比例升高时,最小中心性节点比最大中心性节点更具传播优势.     

Defects’ geometric feature recognition based on infrared image edge detection

Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects’ edges in infrared images effectively enables the identification of defects’ geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects’ edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects’ geometric feature much more completely and clearly. The defects’ diameters have been calculated based on the image edge detection results.     

一种基于Mumford-Shah模型的红外图像边缘检测方法

 Chan-Vese模型是一种优秀的简化Mumford-Shah模型。然而Chan-Vese模型是以两个同质区域为基础建立的,这并不符合红外图像的特点,导致直接应用该模型处理红外图像时可能失败。针对这一问题,提出了一种适用于红外图像边缘检测的改进Mumford-Shah模型,并对该模型中目标边缘的保持、停止准则的建立及算法速度的提高作了详细讨论。实验表明,改进Mumford-Shah模型能够克服Chan-Vese模型在对红外图像边缘检测时不能跨越过渡区域的缺点,有效地检测出目标边缘。     

Two Novel Methods to Enhance Network Synchronizability

In this paper, we propose two methods to enhance the synchronizability of a class of complex networks which do not hold the positive correlation between betweenness centrality （BC） and degree of a node, and observe other topology characteristics of the network affected by the methods. Numerical simulations show that both methods can effectively enhance the synchronizability of this kind of networks. Furthermore, we show that the maximal BC of all edges is an important factor to affect the network synchronizability, although it is not the unique factor.     

改进三帧差分算法在移动物体检测中的应用

移动物体监控系统利用检测算法识别监控区域的移动物体,并进行实时异常信息存储,检测算法中的传统三帧差分法中的阈值是固定的,因此重叠部分无法准确检测出来,存在空洞现象,可能发生误判。针对这些问题,对已有的三帧差分法进行改进,结合图像边缘提取和自适应的迭代阈值计算方法来提高移动物体检测的准确性,并对异常信息进行选择性存储,由实验结果可知,应用平台采用改进后的移动物体检测算法,较好的提高了移动物体检测的灵敏度,增强了检测系统的实时性和准确性,若仅存储异常信息,可节省视频存储空间,并在定位异常动态信息时节省查找时间。     

Detection of community structure in networks based on community coefficients

Determining community structure in networks is fundamental to the analysis of the structural and functional properties of those networks, including social networks, computer networks, and biological networks. Modularity function Q$Q$, which was proposed by Newman and Girvan, was once the most widely used criterion for evaluating the partition of a network into communities. However, modularity Q$Q$ is subject to a serious resolution limit. In this paper, we propose a new function for evaluating the partition of a network into communities. This is called community coefficient C$C$. Using community coefficient C$C$, we can automatically identify the ideal number of communities in the network, without any prior knowledge. We demonstrate that community coefficient C$C$ is superior to the modularity Q$Q$ and does not have a resolution limit. We also compared the two widely used community structure partitioning methods, the hierarchical partitioning algorithm and the normalized cuts (Ncut) spectral partitioning algorithm. We tested these methods on computer-generated networks and real-world networks whose community structures were already known. The Ncut algorithm and community coefficient C$C$ were found to produce better results than hierarchical algorithms. Unlike several other community detection methods, the proposed method effectively partitioned the networks into different community structures and indicated the correct number of communities.     

基于图像边沿特征的强激光腔镜准直处理方法

 激光束光路自动准直系统用于高功率激光装置光束精密准直。基于某激光原型装置总体对腔镜准直调整流程,针对高功率激光系统腔镜准直过程光斑图像,仿真处理并定量分析了光斑边缘特征;结合腔镜准直监测单元光传输分析,使用激光光斑边缘不同部分曲率的相似性和光斑圆形度实现了对腔镜调节的量化评估。在腔镜准直结束时圆形度指标为12.222;边缘相似度达到99.62%。     

基于VB的Solid Edge变量化设计在换热器设计中的应用

在基于Solid Edge软件的基础之上,通过变量化设计建模和编写通用的Visual Basic数据驱动程序,对低温制冷空调设备中常用的各种换热器(管片式、层叠式、平行流式和管带式)进行参数化设计.以管片式换热器为例详细说明软件的三维建模过程,并为今后换热器的优化设计奠定一定的基础.     

Analysis of temperature dependence for a ratiometric wavelength measurement system using SMS fiber structure based edge filters

Temperature dependence of an edge filter based on singlemode-multimode-singlemode (SMS) fiber structure is investigated numerically and experimentally. The experimental results and numerical results are in good agreement within an operational temperature range from 10 °C to 40 °C. It is found that the thermo-optic coefficient (TOC) has a more significant effect on the temperature dependence of an SMS edge filter compared to the thermal expansion coefficient (TEC). In the ratiometric wavelength measurement using two SMS edge filters, a small temperature variation can induce the ratio variation and in turn the wavelength measurement error. It is found the SMS edge filter’s response to both wavelength and temperature is linear. It is proposed that self-monitoring of temperature can be carried out using an updated ratiometric scheme. Self-monitoring of the temperature reduces temperature induced wavelength error to ±0.7 pm at 1545 nm, regardless of the ambient temperature variation.     

Efficient algorithm based on neighborhood overlap for community identification in complex networks

Community structure is an important feature in many real-world networks. Many methods and algorithms for identifying communities have been proposed and have attracted great attention in recent years. In this paper, we present a new approach for discovering the community structure in networks. The novelty is that the algorithm uses the strength of the ties for sorting out nodes into communities. More specifically, we use the principle of weak ties hypothesis to determine to what community the node belongs. The advantages of this method are its simplicity, accuracy, and low computational cost. We demonstrate the effectiveness and efficiency of our algorithm both on real-world networks and on benchmark graphs. We also show that the distribution of link strength can give a general view of the basic structure information of graphs.     

A fast and efficient heuristic algorithm for detecting community structures in complex networks

Community structure is an important property of complex networks. How to detect the communities is significant for understanding the network structure and to analyze the network properties. Many algorithms, such as K-L and GN, have been proposed to detect community structures in complex networks. According to daily experience, a community should have many nodes and connections. Based on these principles and existing researches, a fast and efficient algorithm for detecting community structures in complex networks is proposed in this paper. The key strategy of the algorithm is to mine a node with the closest relations with the community and assign it to this community. Four real-world networks are used to test the performance of the algorithm. Experimental results demonstrate that the algorithm proposed is rather efficient for detecting community structures in complex networks.