Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.

Probability Distribution of Edge in Adjacent Matrix of Aviation Network of China and Algorithm of Searching Non-overlap Community Structure Based on Complex Network

In order to discover the probability distribution feature of edge in aviation network adjacent matrix of China and on the basis of this feature to establish an algorithm of searching non-overlap community structure in network to reveal the inner principle of complex network with the feature of small world in aspect of adjacent matrix and community structure,aviation network adjacent matrix of China was transformed according to the node rank and the matrix was arranged on the basis of ascending node rank with the center point as original point.Adjacent probability from the original point to extension around in approximate area was calculated.Through fitting probability distribution curve,power function of probability distribution of edge in adjacent matrix arranged by ascending node rank was found.According to the feature of adjacent probability distribution,deleting step by step with node rank ascending algorithm was set up to search non-overlap community structure in network and the flow chart of algorithm was given.A non-overlap community structure with 10 different scale communities in aviation network of China was found by the computer program written on the basis of this algorithm.

Dynamic analysis of major public health emergency transmission considering the dual-layer coupling of community–resident complex networks

We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.

Comparative Effects of Avoidance and Immunization on Epidemic Spreading in a Dynamic Small-World Network with Community Structure

Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps.Then an epidemic model of susceptible-infected-recovered is established based on the mean-field method to evaluate the inhibitory effects of avoidance and immunization on epidemic spreading.And an approximate formula for the epidemic threshold is obtained by mathematical analysis.The simulation results show that the epidemic threshold decreases with the increase of inner-community motivation rate and inter-community long-range motivation rate,while it increases with the increase of immunization rate or avoidance rate.It indicates that the inhibitory effect on epidemic spreading of immunization works better than that of avoidance.

Evolutionary Dynamics Modeling of Symbolic Social Network Structure Equilibrium

The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application value.However,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis methods.Based on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.The simulation experiment results show that the calculation method in this paper can get the optimal solution faster.It provides an idea for the study of real and complex social networks.

Emergence of Community Structure in the Adaptive Social Networks

In this paper,we propose a simple model of opinion dynamics to construct social networks,based on the algorithm of link rewiring of local attachment(RLA)and global attachment(RGA).Generality,the system does reach a steady state where all individuals'opinion and the complex network structure are fixed.The RGA enhances the ability of consensus of opinion formation.Furthermore,by tuning a model parameter p,which governs the proportion of RLA and RGA,we find the formation of hierarchical structure in the social networks for p>p_(c).Here,p_(c) is related to the complex network size N and the minimal coordination number 2K.The model also reproduces many features of large social networks,including the“weak links”property.

Community Detection in Dynamic Social Networks

There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.

STUDIES OF THE DYNAMIC BEHAVIORS OF A CLASS OF LEARNING ASSOCIATIVE NEURAL NETWORKS

This paper investigates exponential stability and trajectory bounds of motions of equilibria of a class of associative neural networks under structural variations as learning a new pattern. Some conditions for the possible maximum estimate of the domain of structural exponential stability are determined. The filtering ability of the associative neural networks contaminated by input noises is analyzed. Employing the obtained results as valuable guidelines, a systematic synthesis procedure for constructing a dynamical associative neural network that stores a given set of vectors as the stable equilibrium points as well as learns new patterns can be developed. Some new concepts defined here are expected to be the instruction for further studies of learning associative neural networks.

A New Method Based on Evolutionary Algorithm for Symbolic Network Weak Unbalance

The symbolic network adds the emotional information of the relationship,that is,the“+”and“-”information of the edge,which greatly enhances the modeling ability and has wide application in many fields.Weak unbalance is an important indicator to measure the network tension.This paper starts from the weak structural equilibrium theorem,and integrates the work of predecessors,and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm.Experiments on the large symbolic networks Epinions,Slashdot and WikiElections show the effectiveness and efficiency of the proposed method.In EAWSB,this paper proposes a compression-based indirect representation method,which effectively reduces the size of the genotype space,thus making the algorithm search more complete and easier to get better solutions.

智能算法的亚群优化策略综述

群智能算法的优化是提升群智能算法性能的一个主要途径,随着群智能算法越来越广泛地运用到各类模型优化、生产调度、路径规划等问题中,对智能算法性能的要求也越来越高。亚群策略作为一种优化群智能算法的重要手段,能够灵活地平衡算法的全局勘探能力和局部开发能力,已经成为群智能算法的研究热点之一。为了促进亚群优化策略的发展和应用,对动态亚群策略、基于主从范式的亚群策略和基于网络结构的亚群策略进行了详细调查,阐述了各类亚群策略的结构特点、改进方式和应用场景。最后,总结了亚群策略目前存在的问题以及未来的研究趋势和发展方向。

Complex network analysis in inclined oil-water two-phase flow

Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling many complex natural and artificial systems. Oil-water two-phase flow is one of the most complex systems. In this paper, we use complex networks to study the inclined oil water two-phase flow. Two different complex network construction methods are proposed to build two types of networks, i.e. the flow pattern complex network （FPCN） and fluid dynamic complex network （FDCN）. Through detecting the community structure of FPCN by the community-detection algorithm based on K-means clustering, useful and interesting results are found which can be used for identifying three inclined oil-water flow patterns. To investigate the dynamic characteristics of the inclined oil-water two-phase flow, we construct 48 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of the inclined oil-water two-phase flow. In this paper, from a new perspective, we not only introduce a complex network theory into the study of the oil-water two-phase flow but also indicate that the complex network may be a powerful tool for exploring nonlinear time series in practice.

Community detection with consideration of non-topological information

In a network described by a graph, only topological structure information is considered to determine how the nodes are connected by edges. Non-topological information denotes that which cannot be determined directly from topological information. This paper shows, by a simple example where scientists in three research groups and one external group form four communities, that in some real world networks non-topological information （in this example, the research group affiliation） dominates community division. If the information has some influence on the network topological structure, the question arises as to how to find a suitable algorithm to identify the communities based only on the network topology. We show that weighted Newman algorithm may be the best choice for this example. We believe that this idea is general for real-world complex networks.

改进遗传-狼群对节点序寻优的贝叶斯网络结构算法

贝叶斯网络是数据挖掘领域的一种重要方法。针对贝叶斯网络结构学习算法寻优效率低和易陷入局部最优的问题,提出一种基于改进的混合遗传-狼群对节点序寻优的贝叶斯网络结构学习算法。该算法首先利用深度优先搜索对最大支撑树的节点进行拓扑排序;然后利用动态变异及最优交叉算子构建适用于节点序寻优的改进捕食行为,引入动态参数因子来增强算法局部寻优能力;最后与K2算法结合得到最优的贝叶斯网络结构。用3种不同大小的标准网络数据集中进行实验,结果表明,该算法收敛到较优值,寻优效率高于其它同类优化算法。

面向动态网络的介数中心度并行算法

介数中心度是评价图中节点重要性的一项常用指标,然而在大规模动态图中介数中心度的更新效率很难满足应用需求。随着多核技术的发展,算法并行化已成为解决该问题的有效手段之一。因此,提出一种面向动态网络的介数中心度并行算法(PAB)。首先,通过社区过滤、等距剪枝和分类筛选等操作减少了冗余点对的时间开销;然后,基于对算法确定性的分析和处理实现了并行化。在真实数据集和合成数据集上进行了对比实验,结果显示在添加边更新时PAB的更新效率为并行算法中最新的batch-iCENTRAL的4倍。可见,所提算法能够有效提高动态网络中介数中心度的更新效率。

基于动态网损修正的改进交直流迭代算法

应用于有功经济调度的交直流迭代算法无法在低发电成本条件下达到较低网损的目标。为此在直流最优潮流算法不考虑网损的基础上,借鉴经典经济调度的网损修正理论,并利用交流潮流模型计算网损及网损微增率,提出了动态网损修正方法,可对机组的边际成本参数进行修正。改进的交直流迭代算法在保证算法收敛的前提下,充分逼近了交流最优潮流算法的优化结果。IEEE30节点和IEEE118节点算例验证了该算法的可行性。

结构动力模型修正方法的比较研究及评估

在实际工程中,由结构动力模型得到的计算值与通过试验获得的测量值间往往存在偏差,为了能够精确预测结构的动力响应,依据测量信息修正存在的动力模型是非常必要的.对现有几种有效的用于结构动力模型修正的理论方法（包括基于敏感性分析的矩阵型法、基于神经网络算法的参数型法和基于遗传优化算法的方法）做了详细的综述;介绍了这些方法的步骤和研究进展;并分析了这些动力模型修正方法在工程运用中存在的一些实际问题,如不完整的模态测量值、模型修正的鲁棒性、模型修正的计算效率和收敛性等.最后,通过对一实际的五层钢框架的动力模型修正,比较了这几种方法的优缺点,提出了今后需要解决的问题.

基于P2P的自组织网络路由算法研究

针对传统的P2P采用泛洪的信息传输方式,网络带宽开销耗费较大,而结构化P2P覆盖网又难以在开销和效率方面做到较好的权衡。根据网络的动态性,有效地建立起一个可分层的树型自治系统,详细描述了该系统的构建目标和体系结构,并基于P2P计算模式动态构建该模型,给出相应的路由发现和更新算法。在理论及仿真实验的基础上对该路由模型的性能进行了验证。结果表明,该网络是一种可运行于任何环境,不受限于系统规模大小、节点能力强弱、节点出入频率,可通过动态调节保证路由效率的广域分布式系统。

基于神经网络的动力学反解算法及其应用研究

论述了使用神经网络求解结构动力学反问题的基本原理和方法，并针对ＢＰ网络在使用中存在的主要问题，提出了网络快速学习算法，以及训练样本正交化处理方法和反解结果精度的自适应修正等一系列解决方法。应用上述理论进行了设备动态诊断的数值仿真和试验研究，结果令人满意。

基于结构分解的动态图增量匹配算法

在大数据时代,图数据的规模急剧增长,增量图模式匹配技术能够在数据图发生变化时避免重新对整个数据图进行匹配,进而减少匹配时间,提高整体执行效率,因此成为研究热点。然而,现有的增量匹配算法处理规模较大的模式图时效率会降低。针对该问题,提出了一种基于结构分解的增量图模式匹配算法Inc_CFLS。在匹配过程中,为中间匹配结果构建高效索引,用于后续的模式匹配计算。基于构建的索引信息对数据图增加边事件进行分类,进而为每类增加边事件设计查询剪枝优化策略,从而有效提高匹配效率。在真实数据集上进行实验,结果表明Inc_CFLS算法比目前最好的增量匹配算法在执行效率上平均提升了1~2倍,能更有效支持大规模动态图上的模式匹配。

基于改进鲸鱼优化策略的贝叶斯网络结构学习算法

针对当前贝叶斯网络结构学习算法易陷入局部最优和寻优效率低的问题,该文提出一种基于改进鲸鱼优化策略的贝叶斯网络结构学习算法。该算法首先提出一种新的方法建立较优的初始种群,然后利用不产生非法结构的交叉变异算子构建适用于贝叶斯网络结构学习的改进捕食行为,同时采用动态调节参数增强算法个体寻优的能力,通过适应度排序更新种群,最终获得最优的贝叶斯网络结构。仿真结果表明,该算法具有全局收敛性,寻优效率高,精确率高于其它同类优化算法。