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.

FLOCKING OF A THERMODYNAMIC CUCKER-SMALE MODEL WITH LOCAL VELOCITY INTERACTIONS

In this paper, we study the flocking behavior of a thermodynamic Cucker–Smale model with local velocity interactions. Using the spectral gap of a connected stochastic matrix, together with an elaborate estimate on perturbations of a linearized system, we provide a sufficient framework in terms of initial data and model parameters to guarantee flocking. Moreover, it is shown that the system achieves a consensus at an exponential rate.

Towards sparse matrix operations:graph database approach for power grid computation

The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)technology.The“source-grid-load-storage”architecture of a new power system requires PIoT to have a stronger multi-source heterogeneous data fusion ability.Native graph databases have great advantages in dealing with multi-source heterogeneous data,which make them suitable for an increasing number of analytical computing tasks.However,only few existing graph database products have native support for matrix operation-related interfaces or functions,resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids.In this paper,the matrix computation process is expressed by a strategy called graph description,which relies on the natural connection between the matrix and structure of the graph.Based on that,we implement matrix operations on graph database,including matrix multiplication,matrix decomposition,etc.Specifically,only the nodes relevant to the computation and their neighbors are concerned in the process,which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation.Based on the graph description,a series of power grid computations can be implemented on graph database,which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database.It promotes the efficiency of PIoT when handling multi-source heterogeneous data.An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines.The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.

关于赋权非正则图的A_(α)特征值和特征向量

设G_(ω)=(G,ω)是一个赋权图,其邻接矩阵和赋权度对角矩阵分别A(G_(ω))和D(G_(ω))。对于α∈[0,1],G_(ω)的A_(α)-矩阵为A_(α)(G_(ω))=αD(G_(ω))+(1-α)A(G_(ω))。对于连通赋权非正则图G_(ω),给出了其关于A_(α)-特征值的一些界,并得到了A_(α)-谱半径对应的特征向量中最大分量与最小分量比值的下界。

基于CiteSpace知识图谱的新财经人才培养模式研究

为了促进新财经人才培养模式创新发展,基于宏观视角,借助CiteSpace软件分析近10年新财经人才培养领域的发文特点,梳理出新财经人才培养模式研究的现状、热点与演化脉络,在此基础上提出我国新财经人才培养的具体路径:加强校企合作、校校合作,重点发挥核心机构辐射带动作用;开展“新财经+地方特色”教学培养模式;全方位立体化建立纵深式产教融合培养模式;深化学科融合力度,推动新财经人才综合素质培养。

Investigation on Singularity, Signature Matrix and Spectrum of Mixed Graphs

The spectral theory of graph is an important branch of graph theory,and the main part of this theory is the connection between the spectral properties and the structural properties,characterization of the structural properties of graphs.We discuss the problems about singularity,signature matrix and spectrum of mixed graphs.Without loss of generality,parallel edges and loops are permitted in mixed graphs.Let G1 and G2 be connected mixed graphs which are obtained from an underlying graph G.When G1 and G2 have the same singularity,the number of induced cycles in Gi(i=1,2)is l(l=1,l>1),the length of the smallest induced cycles is 1,2,at least 3.According to conclusions and mathematics induction,we find that the singularity of corresponding induced cycles in G1 and G2 are the same if and only if there exists a signature matrix D such that L(G2)=DTL(G1)D.D may be the product of some signature matrices.If L(G2)=D^TL(G1)D,G1 and G2 have the same spectrum.

New matrix of MALDI-TOF MS for analysis of small molecules

New matrix, metal-phthalocyanine （MPc）, of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry （MALDI-TOF MS） was used for analysis of small molecules （usually 〈500 Da）. By using MPcs as matrices, small molecular samples were moved to high mass-to-charge region where there was no interference caused by the traditional matrices. The mass of the target analvte was obtained by simple calculation.

Analyzing the role of extracellular matrix during nervous system development to advance new regenerative strategies

Regeneration in the central nervous system （CNS） is limited, and CNS damage often leads to cognitive impairment or permanent functional motor and sensory loss. Impaired regenerative capacity is multifactorial and includes inflammation, loss of the blood-brain barrier, and alteration in the extracellular matrix （ECM）. One of the main problems is the formation of a glial scar and the production of inhibitory ECM, such as proteoglycans, that generates a physical and mechanical barrier, impeding axonal regrowth （Figure 1A）.

A NEW CLASS OF LOCALLY CONVEX SPACES AND THE GENERALIZATION OF KALTON'S CLOSED GRAPH THEOREM

By introducing the notions of L-spaces and L_r-spaces, a complete generalization of Kalton's closed graph theorem is obtained. It points out the class of L_r-spaces is the maximal class of range spaces for the closed graph theorem when the class of domain spaces is the class of Mackey spaces with weakly * sequentially complete dual.Some examples are constructed showing that the class of L_r-spaces is strictly larger than the class of separable B_r-complete spaces.Some properties of L-spaces and L_r-spaces are discussed and the relations between B-complete (resp. B_r-complete) spaces and L-spaces (resp. L_r-spaces) are given.

基于Distance-2算法的并行Jacobian矩阵计算及其在耦合问题中的应用

并行Newton-Krylov方法是求解大规模多物理耦合问题的有效方法,如何高效自动计算Jacobian矩阵是一大难点。利用有限差分方法,可避免推导Jacobian矩阵的表达式,实现矩阵的自动计算。现有工作表明,在串行环境下利用矩阵的稀疏性和图着色算法,Jacobian矩阵的计算效率可提高至少1个量级。但在并行环境下,串行着色算法失效,需采用相应的并行着色算法。本研究将图论领域的Distance-2算法应用于Jacobian矩阵的并行着色。通过求解一个简化多物理耦合问题检验了该并行算法的正确性和计算效率。测试结果表明,该并行算法得到的Jacobian矩阵完全正确;着色数随着并行核数的增加略微有所增加,100个进程下并行效率为56%;基于该算法求解多物理耦合问题,其计算时间和Krylov迭代次数较JFNK减少了约1/2。

Graph Theory and Matrix Approach(GTMA)Model for the Selec­tion of the Femoral-Component of Total Knee Joint Replacement

Total Knee Replacement(TKR)is the increasing trend now a day,in revision surgery which is associated with aseptic loosening,which is a challenging research for the TKR component.The selection of optimal material loosening can be controlled at some limits.This paper is going to consider the best material selected among a number of alternative materials for the femoral component(FC)by using Graph Theory.Here GTMA process used for optimization of material and a systematic technique introduced through sensitivity analysis to find out the more reliable result.Obtained ranking suggests the use of optimized material over the other existing material.By following GTMA Co_Cr-alloys(wrought-Co-Ni-Cr-Mo)and Co_Cr-alloys(cast-able-Co-Cr-Mo)are on the 1st and 2nd position respectively.

双星图的LI矩阵的Ky Fan k-范数

树是连通的无圈图,研究树的拉普拉斯矩阵具有重要的图论和实际意义.设G是一个有n个点和m个边的图,A(G)和D(G)分别是图G的邻接矩阵和对角度矩阵,那么G的拉普拉斯矩阵定义为L(G)=D(G)-A(G).LI矩阵定义为LI(G)=L(G)-(2m/n)I_(n),其中I_(n)是单位矩阵.图的LI矩阵的Ky Fan k-范数代表了拉普拉斯特征值和拉普拉斯特征值平均值之间距离的有序和.研究了双星图的LI矩阵的Ky Fan k-范数,证明了双星图的LI矩阵的Ky Fan k-范数满足文献[6]中提出的猜想.

新生代农民工研究现状与动态趋势——基于CiteSpace的文献计量分析

新生代农民工是推进中国式现代化和乡村振兴战略的重要主体。基于CiteSpace软件,对2005—2023年国内新生代农民工的相关核心文献进行文献计量以及可视化分析。研究发现:新生代农民工研究领域尚未形成成熟的作者合作团队;研究热点可归纳为新生代农民工的特征与代际差异、新生代农民工市民化研究、返乡创业研究3个方面;当前研究主要集中于城乡迁移、回流意愿、内生增长、城乡融合方面。

基于行为特征和语义特征的多模态Android恶意软件检测方法

现有的Android恶意软件检测方法只考虑单一种类的特征,并不能全面描述Android软件的特征。为解决此类问题,文中从权限、字节码概率矩阵和函数调用图3种类型特征出发,提出了一种基于行为特征和语义特征的多模态Android恶意软件检测方法。同时,为了解决函数节点特征表示问题,文中针对函数调用图的生成过程提出了一种新的节点特征生成方法。为了丰富操作码语义信息,提出了一种基于2-gram的字节概率矩阵生成方法。通过实验证明了文中方法相较于其他方法可更加全面地描述Android软件的特征,检测准确率达到95.2%,相较于已有方法准确率平均提升了22%,有效提高了Android恶意软件的检测能力。

A New Modification to Shear Lag Model as Applied to Stiffness and Yield Strength of Short Fiber Reinforced Metal Matrix Composites

A new modification for the shear lag model is given and the expressions for the stiffness and yield Strength of short fiber metal matri×composite are derived. These expressions are then compared with our experimental data in a SiCw/Al-Li T6 composite and the published experimental data on different SiCw/Al T6 composites and also compared with the previous shear lag models and the other theoretical models.

Identifying multiple influential spreaders in complex networks based on spectral graph theory

One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network.The importance and dispersion of critical nodes among them are both vital factors that can influence maximization.We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory.This algorithm first quantifies the role played by the local structure of nodes in the propagation process,then classifies the nodes based on the eigenvectors of the Laplace matrix,and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other.Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.

基于CiteSpace的农产品直播电商研究热点及趋势的可视化分析

乡村振兴背景下,农村电商正逐步实现规模化发展,并正向直播电商模式转型。本文借助Citespace文献计量工具,选取CNKI和中国知网学术期刊网络出版总库的CSSCI期刊和中文核心期刊中945篇相关文献进行网络共现分析和可视化,探索农产品直播电商研究热点和发展趋势。研究表明:当前农产品电商直播的有关研究正处于上升期,各研究人员和机构之间的合作关系有待加强;研究热点涵盖网络直播、直播平台、电商扶贫、农业电商、新农人等;研究趋势朝“新农人”、技术创新及质量融合等方向发展。未来相关研究应致力于不断完善农产品直播电商的理论框架,拓宽研究领域,并加强跨学科的研究合作,以推动该领域的全面深入发展。

基于Citespace的中国年画艺术热点与发展趋势研究

运用Citespace文献计量软件对1998-2023年中国知网(CNKI)收录的351篇年画艺术研究文献进行知识图谱分析,探讨国内学界关于年画艺术的研究现状、热点及未来研究趋势,以期为后续研究提供参考。

基于CiteSpace文献计量分析法的我国新型职业农民研究进展

新型职业农民代表一种主动的职业选择,是实现中国式农业农村现代化的智力支撑。基于CiteSpace软件对2006—2022年CNKI网络数据库中新型职业农民的研究文献进行可视化分析,结果表明:(1)发文量呈现“初步萌芽期(2006—2011年)—暴发增长期(2012—2015年)—稳定发展期(2016—2022年)”发展趋势,整体发文量波动上升。作者分布广、结构松散、合作关系不够紧密,呈现分散状态。涉及的学科主要分布在成人教育与特殊教育、农业经济、职业教育等领域。(2)研究热点主要集中在“新型职业农民的内涵、特征及其意义”“新型职业农民的成长与培育问题”“新型职业农民的素质与能力探析”等方面。今后要重点关注新型职业农民的自身素质提升与能力建设、新型职业农民培育转型升级等领域。

新型举国体制的研究热点及前沿--基于CiteSpace的可视化图谱分析

为系统梳理新型举国体制的研究进展与演进历程,文章以2001-2023年CNKI数据库收录的278篇CSSCI和核心期刊论文作为研究样本,利用CiteSpace可视化文献分析软件,分别从年度发文量趋势、发文作者、突现词探测、关键词共现等多个维度,分析了我国新型举国体制研究的演进脉络、热点内容、演进趋势。通过对相关图谱的解读,可知我国新型举国体制研究进程大致可划分为概念界定与夯实阶段、区块化研究阶段和深化研究应用阶段;研究热点逐步从竞技体育延伸至社会主义市场经济,进一步扩展到重大核心技术攻关、科技创新领域等;存在相关实证研究较少、高校基础研究建设相关研究不足等薄弱之处,仍需后续研究者深入探索。