基于三角模体的社团发现算法

为了提高社团发现算法的效率,提出了一种基于三角模体和期望极大的社团结构发现(Community structure discovery based on triangular motifs and expectation-maximization,CSDTME)模型的社团发现算法。CSDTME模型采用三角模体对网络进行表示,考虑了节点的混合隶属度及社团间的链接关系,用期望极大算法计算模型涉及的参数,采用全三角模体和两边三角模体作为计算对象,通过减少计算对象来提高算法的效率,根据参数结果可得到节点的社团隶属度及社团间的链接关系。实验结果表明:在保证社团发现能力的同时,该算法能够提高社团发现的效率。

大规模网络的三角形模体社区发现模型

研究表明将边表示的网络转换为三角形模体表示形式,可以有效解决基于模型社区发现方法由网络规模庞大带来的计算瓶颈问题.提出一个三角形模体社区发现模型MCDTM(a Model for Community Detection based on Triangular Motifs),其将网络表示为一系列三角形模体,利用categorical分布对各三角形模体的生成过程建模,用最大似然参数估计方法给出参数估计的推理过程,根据参数估计结果可得节点、边及三角形模体的社区隶属度.人工网络和实际网络上的实验证明MCDTM模型可快速准确地发现网络的潜在结构.

基于概率模型的大规模网络结构发现方法

随着万维网和在线社交网站的发展,规模大、结构复杂、动态性强的大规模网络应用而生.发现这些网络的潜在结构,是分析和理解网络数据的基本途径.概率模型以其灵活的建模和解释能力、坚实的理论框架成为各领域研究网络结构发现任务的有效工具,但该类方法存在计算瓶颈.近几年出现了一些基于概率模型的大规模网络结构发现方法,主要从网络表示、结构假设、参数求解这3个方面解决计算问题.按照模型参数求解策略将已有方法归为两类:随机变分推理(stochastic variational inference)方法和在线EM(online expectation maximazation)方法,详细分析各方法的设计动机、原理和优缺点.定性和定量地对比、分析典型方法的特点和性能,并提出大规模网络结构发现模型的设计原则.最后,概括该领域研究的核心问题,展望未来发展趋势.

Indications of c-axis Charge Transport in Hole Doped Triangular Antiferromagnets

The c-axis charge transport of the hole doped triangular antiferromagnet is investigated within the t-J model by considering the incoherent interlayer hopping. It is shown that the c-axis charge transport of the hole doped triangular antiferromagnet is essentially determined by the scattering from the in-plane fluctuation. The c-axis conductivity spectrum shows a low-energy peak and the unusual high-energy broad band, while the c-axis resistivity is characterized by a crossover from the high temperature metallic-like behavior to the low temperature insulating-like behavior, which is qualitatively consistent with those of the hole doped square lattice antiferromagnet.

A Triangular Model for Social Work Groups： Teaching the Forgotten Practice Modality

Social Work students typically enroll in the required practice methods courses that are specified by the Council on Social Work Education. At least in the majority of BSW Programs, these practice methods courses are divided into micro-level and macro-level sequences. Individual and family systems are covered in the former sequence, while communities and organizations are the systems of focus in the latter sequence. The group system is often minimized and/or completely ignored in the process, even though there may be some reference made to groups in both practice methods sequences. Consequently, the mezzo-level system of groups is only given ＂lip-service＂ in the dyadic division of micro and macro-level practice. Furthermore, students often do not have the opportunity to practice in group settings, much less, develop competency in these professional venues. This paper describes a teaching format used in a BSW Program in an effort to ensure student competency as required in a Groups Practice course. The triangular model includes： （1） Classroom teaching of theory; （2） Practice of group dynamics; and （3） Supervision by instructors and MSW students.

Voxelizating 3D Mesh Models with Gray Levels

A novel algorithm to voxelize 3D mesh models with gray levels is presented in this paper.The key innovation of our method is to decide the gray level of a voxel according to the total area of all surfaces contained by it.During the preprocessing stage,a set of voxels in the extended bounding box of each triangle is established.Then we travel each triangle and compute the areas between it and its set of voxels one by one.Finally,each voxel is arranged a discrete gray level from 0 to 255.Experiments show that our algorithm gets a comparatively perfect result compared with the prevenient ones and approaches the original models in a more accurate way.