双模网络下基于节点流行度的潜在空间模型

本文提出了双模网络下基于节点流行度的潜在空间模型，不仅能够显式地表达节点间产生连接的概率，而且可以推导出双模网络的连接的传递性、节点度的异质性等特征，这些特征可以通过数值化定量的方式描述网络生成过程中的常见规律。在此基础之上，本文进一步提出了加权概率指标，用以衡量双模网络的节点间未来产生连接的可能性。最后，本文分别在模拟数据、公开数据集和某在线点评网站的商户一消费者网络数据上验证了模型假设符合实际数据的分布，并使用加权概率指标与其他多种双模网络链路预测的方法进行比较分析。实验结果表明，本文提出的方法不仅可以量化分析网络生成过程中的特征，而且在实验数据上的链路预测能力整体优于其他双模链路预测方法。     

网络节点重要度评价方法研究

文章通过对已有网络节点重要性评价指标在计算复杂度、体现节点属性等方面缺陷进行分析,提出了基于节点的度和凝聚度线性加权的节点的重要性测度指标;以网络信息连通性和连通成本为目标,描述了节点重要度概念;论证了度和凝聚度与网络平均最短距离的等价性;通过节点度和凝聚度的权重设计,体现了实际网络节点间的不同属性。文章还用供应链网络案例说明了该方法的有效性和优越性。     

基于复杂网络视角下省际人口迁移空间格局及趋势研究

本文提出一套省际人口迁移空间格局及演变趋势研究系统,以省级行政区和迁移人口构建复杂网络模型,应用群集发现和互信息节点重要性评估的算法对人口迁移空间格局进行分析,同时引入人口迁移选择指数改进链路预测算法来预测迁移人口的潜在趋势.在此基础上,以第六次全国人口普查数据为例进行实证分析,其结果与已有的研究及我国省际人口迁移空间格局的现实状况相符,验证了这套方法的实践性和应用价值.     

基于高斯谱聚类的风险商户聚类分析

随着电子支付的普及,市场涌现出越来越多的第三方支付平台,而当前关于第三方支付平台商户风险方面的研究相对较少。故本文提出基于高斯谱聚类的风险商户聚类方法,首先使用高斯混合模型构建交易-交易群体的双模网络;其次借助网络中信息传递的思想构建“商户-交易群体网络”的双模网络;再次使用双模网络聚类方法中的谱聚类方法同时对网络中的两类节点聚类,对商户节点聚类的结果可区分出不同风险级别的商户,对交易群体节点聚类的结果可以进一步描述风险商户的交易特征;最后本文分别在模拟数据和某第方支付平台的实际数据中验证了模型的有效性。实验结果表明,本文提出的方法不仅可以准确地区分出不同风险级别的商户群体,而且能总结归纳风险商户的交易特征,为风险商户的监管提供参考。     

基于尾部风险关联网络的中国金融机构间风险溢出效应研究

基于尾部风险溢出思想,采用2007-2017年的周收益率数据,运用CoVaR模型测度银行、证券、保险及房地产四行业40家上市公司之间的风险溢出效应,并结合系统性风险指数,得出各机构风险吸收与扩散能力排名。运用极大平面过滤图(PMFG)算法对风险溢出网络进行化简,构建仅包含关键路径信息的ΔCoVaR有向加权风险溢出网络,并结合网络中节点关联特征指标,为系统重要性金融机构的有效识别提供了全新视角。总体来看,各行业依风险传递方向不同而对风险的敏感程度各异,网络中关键节点对系统的整体结构影响较强;证券公司内部风险关联较为紧密,房地产机构承受行业间风险多源自银行业,大型国有商业银行对其他行业的风险发散能力较强。     

公共组织网络应对自然灾害救助协同模型设计

目前公共组织在自然灾害救助中开始显现出一种模糊的网络运行机制,这种机制有别于传统的纵向或横向行政管理模式。通过设定必要的假设条件,建立符合复杂网络特征的数学模型,验证了在网络运行过程中节点间的最短路径长度与交流频率大致呈现倒数关系,较高的聚类系数可以提高节点协同程度从而降低交流成本,网络中各节点联系紧密程度的信任值是影响网络信息传播程度的重要指标。     

社会网络分析视角下复杂网络结构关系的综合测度

为了克服复杂网络节点数量繁多、结构关系不易测度的难题,文章提出一种社会网络分析视角下“以点界面”的研究思路.通过构建一个新的综合测度指标,完成对网络中节点的综合评价,实现对网络结构关系的测度.与现有社会网络单一测度指标相比,综合测度指标突破现有单一测度指标的局限,构建过程充分利用单一测度指标间关系规律.复杂网络仿真实验进一步表明,无论有中心结构的网络结构和无中心结构的网络结构,综合测度指标提高网络节点不同评价维度和网络角色自带信息的利用率,对网络中节点的全面刻画更为贴切,为复杂网络结构关系综合测度研究提供一种新工具,为提高生活生产效率、预防灾害负面影响提供参考和依据.     

社会核算矩阵平衡方法研究

本文针对双比例尺度(RAS)、交叉熵(CE)等方法在平衡社会核算矩阵(SAM)中仅从技术层面机械地进行平衡化处理致使先验信息损失的问题,提出了加权离差熵平方期望最小化方法;并以先验信息为基础,构造了初始加权矩阵和可行加权矩阵.同时,本文以中国2007年的非平衡SAM为例,对比研究RAS、CE和加权离差熵平方期望最小化三种方法对其进行平衡化处理的实际效果.结果表明:RAS方法得到的结果偏差相对较大,而CE方法和加权离差熵平方期望最小化方法得到的结果相对较精准;此外,加权离差熵平方期望最小化方法能够有效利用先验信息,避免有效信息的无谓损失.     

产业集群知识网络绩效研究

文章从产业集群的关系网络出发,基于加权小世界网络,研究产业集群的知识网络绩效。提出用节点间的传输距离、传输频率、知识价值量、知识构建维护成本来衡量产业集群知识网络的绩效,并得出了其对产业集群知识网络绩效的影响函数。     

基于高维波动率网络模型的股票市场风险特征研究

波动率是金融风险管理研究的重要内容之一。本文基于复杂网络理论和数据挖掘技术提出股票市场的高维波动率网络模型。首先运用互信息度量不同股票价格波动之间的相关关系，其次对股票市场不同周期下的波动情况建立度的中心势、平均距离、幂律分布等网络拓扑指标，再次根据这些指标利用Prim算法构建出高维波动率网络模型，最后运用Newman-Girvan算法对股票价格波动率的相关性进行分层研究。高维波动率网络模型突破了传统波动率模型关于变量维数的限制，能够在依赖少量假设的基础上，挖掘出多个金融市场主体间的相互关系，反映金融市场的风险特征及网络拓扑性质。实证结果发现：与常用的Pearson相关系数法相比，在互信息框架下，股价波动的非线性相关关系得到了更好的度量；股票市场的整体波动性与个股波动率相关性变化趋势相反，市场处在高波动时期资产组合分散化效果较好；网络中存在少量度数大的关键节点和中心节点，风险通过这些节点可以迅速传递到整个市场；股票市场的运行具有明显的行业聚集现象；网络分层研究进一步直观的展现了风险在层与层之间的传递规律和与之对应的行业特征。高维波动率网络模型为挖掘股票市场的风险特征与管理金融风险提供了一个新的工具。     

Affiliation discrete weighted networks with an increasing degree sequence

Affiliation network is one kind of two-mode social network with two different sets of nodes (namely, a set of actors and a set of social events) and edges representing the affiliation of the actors with the social events. The connections in many affiliation networks are only binary weighted between actors and social events that can not reveal the affiliation strength relationship. Although a number of statistical models are proposed to analyze affiliation binary weighted networks, the asymptotic behaviors of the maximum likelihood estimator (MLE) are still unknown or have not been properly explored in affiliation weighted networks. In this paper, we study an affiliation model with the degree sequence as the exclusively natural sufficient statistic in the exponential family distributions. We derive the consistency and asymptotic normality of the maximum likelihood estimator in affiliation finite discrete weighted networks when the numbers of actors and events both go to infinity. Simulation studies and a real data example demonstrate our theoretical results.     

大数据背景下基于社交网络的聚类随机游走抽样算法研究

数字经济时代,社交网络作为数字化平台经济的重要载体,受到了国内外学者的广泛关注。大数据背景下,社交网络的商业应用价值巨大,但由于其网络规模空前庞大,传统的网络分析方法 因计算成本过高而不再适用。而通过网络抽样算法获取样本网络,再推断整体网络,可节约计算资源, 因此抽样算法的好坏将直接影响社交网络分析结论的准确性。现有社交网络抽样算法存在忽略网络内部拓扑结构、容易陷入局部网络、抽样效率过低等缺陷。为了弥补现有社交网络抽样算法的缺陷,本文结合大数据社交网络的社区特征,提出了一种聚类随机游走抽样算法。该方法首先使用社区聚类算法将原始网络节点进行社区划分,得到多个社区网络,然后分别对每个社区进行随机游走抽样获取样本网 络。数值模拟和案例应用的结果均表明,聚类随机游走抽样算法克服了传统网络抽样算法的缺点,能够在降低网络规模的同时较好地保留原始网络的结构特征。此外,该抽样算法还可以并行运算,有效提升抽样效率,对于大数据背景下大规模社交网络的抽样实践具有重大现实意义。     

Dynamic stochastic block models: parameter estimation and detection of changes in community structure

The stochastic block model (SBM) is widely used for modelling network data by assigning individuals (nodes) to communities (blocks) with the probability of an edge existing between individuals depending upon community membership. In this paper, we introduce an autoregressive extension of the SBM, based on continuous-time Markovian edge dynamics. The model is appropriate for networks evolving over time and allows for edges to turn on and off. Moreover, we allow for the movement of individuals between communities. An effective reversible-jump Markov chain Monte Carlo algorithm is introduced for sampling jointly from the posterior distribution of the community parameters and the number and location of changes in community membership. The algorithm is successfully applied to a network of mice.     

Frailty effects in networks: comparison and identification of individual heterogeneity versus preferential attachment in evolving networks

Preferential attachment is a proportionate growth process in networks, where nodes receive new links in proportion to their current degree. Preferential attachment is a popular generative mechanism to explain the widespread observation of power-law-distributed networks. An alternative explanation for the phenomenon is a randomly grown network with large individual variation in growth rates among the nodes (frailty). We derive analytically the distribution of individual rates, which will reproduce the connectivity distribution that is obtained from a general preferential attachment process (Yule process), and the structural differences between the two types of graphs are examined by simulations. We present a statistical test to distinguish the two generative mechanisms from each other and we apply the test to both simulated data and two real data sets of scientific citation and sexual partner networks. The findings from the latter analyses argue for frailty effects as an important mechanism underlying the dynamics of complex networks.     

A statistical approach to social network monitoring

Social network monitoring consists of monitoring changes in networks with the aim of detecting significant ones and attempting to identify assignable cause(s) contributing to the occurrence of a change. This paper proposes a method that helps to overcome some of the weaknesses of the existing methods. A Poisson regression model for the probability of the number of communications between network members as a function of vertex attributes is constructed. Multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts are used to monitor the network formation process. The results indicate more efficient performance for the MEWMA chart in identifying significant changes.     

NETWORK EXPLORATION VIA THE ADAPTIVE LASSO AND SCAD PENALTIES

Graphical models are frequently used to explore networks, such as genetic networks, among a set of variables. This is usually carried out via exploring the sparsity of the precision matrix of the variables under consideration. Penalized likelihood methods are often used in such explorations. Yet, positive-definiteness constraints of precision matrices make the optimization problem challenging. We introduce non-concave penalties and the adaptive LASSO penalty to attenuate the bias problem in the network estimation. Through the local linear approximation to the non-concave penalty functions, the problem of precision matrix estimation is recast as a sequence of penalized likelihood problems with a weighted L(1) penalty and solved using the efficient algorithm of Friedman et al. (2008). Our estimation schemes are applied to two real datasets. Simulation experiments and asymptotic theory are used to justify our proposed methods.     

The Relative Fit measure for evaluating a blockmodel

Statistical Methods & Applications - A blockmodel is a network in which the nodes are clusters of equivalent (in terms of the structure of the links connecting) nodes in the network being...     

An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine

The article focuses on the application of the Bayesian networks (BN) technique to problems of personalized medicine. The simple (intuitive) algorithm of BN optimization with respect to the number of nodes using naive network topology is developed. This algorithm allows to increase the BN prediction quality and to identify the most important variables of the network. The parallel program implementing the algorithm has demonstrated good scalability with an increase in the computational cores number, and it can be applied to the large patients database containing thousands of variables. This program is applied for the prediction for the unfavorable outcome of coronary artery disease (CAD) for patients who survived the acute coronary syndrome (ACS). As a result, the quality of the predictions of the investigated networks was significantly improved and the most important risk factors were detected. The significance of the tumor necrosis factor-alpha gene polymorphism for the prediction of the unfavorable outcome of CAD for patients survived after ACS was revealed for the first time.     

Predicting profitability using advice branch bank networks

The literature on social networks and their analysis has undergone explosive growth in the past decade. Network models have been used to study structures as diverse as the interaction of monks in a monastery, the links across the World Wide Web, and the structure of organizations. In much of this literature the network itself is viewed as the object of interest, and models are used to elucidate its structure. In this paper, we adopt a different perspective and we explore the role of network structure of organizations for prediction purposes. In particular, we work with data gathered on the advice-seeking habits of employees in 52 branches of a major North American bank corporation. We then use the network structure within each branch discovered via various exploratory analyses to predict the profitability of the individual branches.