可调聚类系数加权无标度网络建模及其拥塞问题研究

针对实现网络特征的真实情况,提出了一类可调聚类系数的加权无标度网络模型,该模型能够重现现实网络权重和节点度呈幂律分布的统计特性．特别是聚类系数与度之间的非线性关系,恰好符合某些现实网络聚类系数与度之间的平头关系特征．最后研究了可调聚类系数加权网络模型中的拥塞问题．采用基于强度优先传递的局部路由策略,分析了网络中的流量传输问题．     

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DEA的竞争战略识别

针对有效决策单元评价和区分的问题,在充分提取决策单元之间相似性和相异性信息基础上,定义了多指标区间交叉效率,进而提出了一种基于投入、产出权重的聚类分析方法,并将其应用于竞争战略识别.实证结果表明,该方法能够区分有效决策单元,综合评价具有统一性和合理性;与同类战略识别方法相比,更具客观性和解释能力,分类效果更好.该方法提供了一种客观的新的竞争战略识别方法,有助于战略有效性的研究.     

Particle swarm optimization with age-group topology for multimodal functions and data clustering

This paper proposes particle swarm optimization with age-group topology (PSOAG), a novel age-based particle swarm optimization (PSO). In this work, we present a new concept of age to measure the search ability of each particle in local area. To keep population diversity during searching, we separate particles to different age-groups by their age and particles in each age-group can only select the ones in younger groups or their own groups as their neighbourhoods. To allow search escape from local optima, the aging particles are regularly replaced by new and randomly generated ones. In addition, we design an age-group based parameter setting method, where particles in different age-groups have different parameters, to accelerate convergence. This algorithm is applied to nonlinear function optimization and data clustering problems for performance evaluation. In comparison against several PSO variants and other EAs, we find that the proposed algorithm provides significantly better performances on both the function optimization problems and the data clustering tasks.     

Detecting community structure using biased random merging

Decomposing a network into small modules or communities is beneficial for understanding the structure and dynamics of the network. One of the most prominent approaches is to repeatedly join communities together in pairs with the greatest increase in modularity so that a dendrogram that shows the order of joins is obtained. Then the community structure is acquired by cutting the dendrogram at the levels corresponding to the maximum modularity. However, there tends to be multiple pairs of communities that share the maximum modularity increment and the greedy agglomerative procedure may only merge one of them. Although the modularity function typically admits a lot of high-scoring solutions, the greedy strategy may fail to reach any of them. In this paper we propose an enhanced data structure in order to enable diverse choices of merging operations in community finding procedure. This data structure is actually a max-heap equipped with an extra array that stores the maximum modularity increments; and the corresponding community pairs is merged in the next move. By randomly sampling elements in this head array, additional diverse community structures can be efficiently extracted. The head array is designed to host the community pairs corresponding to the most significant increments in modularity so that the modularity structures obtained out of the sampling exhibit high modularity scores that are, on the average, even greater than what the CNM algorithm produces. Our method is tested on both real-world and computer-generated networks.     

Modeling the clustering in citation networks

For the study of citation networks, a challenging problem is modeling the high clustering. Existing studies indicate that the promising way to model the high clustering is a copying strategy, i.e., a paper copies the references of its neighbor as its own references. However, the line of models highly underestimates the number of abundant triangles observed in real citation networks and thus cannot well model the high clustering. In this paper, we point out that the failure of existing models lies in that they do not capture the connecting patterns among existing papers. By leveraging the knowledge indicated by such connecting patterns, we further propose a new model for the high clustering in citation networks. Experiments on two real world citation networks, respectively from a special research area and a multidisciplinary research area, demonstrate that our model can reproduce not only the power-law degree distribution as traditional models but also the number of triangles, the high clustering coefficient and the size distribution of co-citation clusters as observed in these real networks.     

基于模糊聚类算法的苯系物二次有机气溶胶质谱数据分析

OH自由基启动苯系物光氧化形成的二次有机气溶胶能够影响气候变化,降低大气能见度,危害人类健康。我们实验室自行研制的气溶胶飞行时间质谱仪能够用于二次有机气溶胶的实时检测研究。该仪器在检测过程中产生大量的实验数据,对这些数据快速、自动处理并提取有价值的信息是整机系统的关键之一。本文介绍模糊聚类算法在苯系物二次有机气溶胶单粒子聚类分析中的成功运用。利用该算法对OH自由基启动1,3,5-三甲苯光氧化形成的二次有机气溶胶单粒子质谱数据进行了聚类分析,在得到的五个聚类中包含了芳香醛、酚类化合物、硝基酚和羧酸等重要的光氧化产物,为研究苯系物光氧化机理提供了新的信息。     

Three-dimensional elliptic grid generation with fully automatic boundary constraints

A new procedure for generating smooth uniformly clustered three-dimensional structured elliptic grids is presented here which formulates three-dimensional boundary constraints by extending the two-dimensional counterpart¹ presented by the author earlier. This fully automatic procedure obviates the need for manual specification of decay parameters over the six bounding surfaces of a given volume grid. The procedure has been demonstrated here for the Mars Science Laboratory (MSL) geometries such as aeroshell and canopy, as well as the Inflatable Aerodynamic Decelerator (IAD) geometry and a 3D analytically defined geometry. The new procedure also enables generation of single-block grids for such geometries because the automatic boundary constraints permit the decay parameters to evolve as part of the solution to the elliptic grid system of equations. These decay parameters are no longer just constants, as specified in the conventional approach, but functions of generalized coordinate variables over a given bounding surface. Since these decay functions vary over a given boundary, orthogonal grids around any arbitrary simply-connected boundary can be clustered automatically without having to break up the boundaries and the corresponding interior or exterior domains into various blocks for grid generation. The new boundary constraints are not limited to the simply-connected regions only, but can also be formulated around multiply-connected and isolated regions in the interior. The proposed method is superior to other methods of grid generation such as algebraic and hyperbolic techniques in that the grids obtained here are C² continuous, whereas simple elliptic smoothing of algebraic or hyperbolic grids to enforce C² continuity destroys the grid clustering near the boundaries.     

An effect of turbulent clustering on scattering of microwave radiation by small particles in the atmosphere

A coherent scattering of electromagnetic waves by clusters of inertial Rayleigh particles in atmospheric turbulence is considered. A preliminary estimate based on the Maxwell-Garnett theory and the Rayleigh approximation for single clusters demonstrates an importance of the coherent scattering contribution. It is confirmed by a general solution in a combination with theoretical estimates for the two-point probability density function for low-inertia spherical particles in isotropic turbulence. An approximate analytical expression for the coefficient characterizing effect of coherent scattering by the particle clusters is derived. The calculations for small Stokes numbers typical of water droplets in cumulus clouds yield an estimate of the coherent scattering effect on the microwave radar reflection. The model suggested allows solving the inverse problem to determine the pair correlation function for cloud particles. It is expected to be important for the investigations on particle–turbulence interaction in the atmosphere. The theoretical model developed is true not only in the limit of low-inertia particles and can be potentially used at arbitrary Stokes numbers in other applications.     

Optimization based DC programming and DCA for hierarchical clustering

One of the most promising approaches for clustering is based on methods of mathematical programming. In this paper we propose new optimization methods based on DC (Difference of Convex functions) programming for hierarchical clustering. A bilevel hierarchical clustering model is considered with different optimization formulations. They are all nonconvex, nonsmooth optimization problems for which we investigate attractive DC optimization Algorithms called DCA. Numerical results on some artificial and real-world databases are reported. The results demonstrate that the proposed algorithms are more efficient than related existing methods.