Rangefinder Box Plots: A Note

This note introduces the rangefinder box plot, an extension of the familiar box plot.     

Hypercubic designs and applications

In this paper we develop relatively easy methods for constructing hypercubic designs from symmetrical factorial experiments for t=v ^m treatments with v=2, 3. The proposed methods are easy to use and are flexible in terms of choice of possible block sizes.     

Exponential random graph models for management research: A case study of executive recruitment

We introduce a recent development in the statistical analysis of relational data that offers rigorous discrimination of a variety of structural and behavioural effects of interest to management research. Exponential random graph models account for the highly interdependent nature of network data that are problematic for the predominant inferential statistical analysis used in management research. We illustrate the value of the approach with an application focused on executive recruitment by large UK firms, modelling migrations of managers among firms as a network of relationships. We find rigorous statistical support for the influences of industry origin in executive recruitment, particularly in relation to legal and accounting activities. The flexibility and sophisticated relational variables available in the models offer considerable analytical power of value to a wide range of management applications.     

Social networks and spatial configuration—How office layouts drive social interaction

This paper analyzes the spatial dimensions of office layouts in diverse knowledge-intensive workplace environments based on the theoretical and methodological propositions of Space Syntax, and brings this together with the analysis of intra-organizational interaction networks. Physical distances between agents are modeled in different ways and used as explanatory variables in exponential random graph modeling. The paper shows that spatial configuration in offices can be considered an important but not sole rationale for tie formation. Furthermore, it is shown that spatial distance measures based on detailed configurational analysis outperform simple Euclidean distance metrics in predicting social ties.     

A new scalable leader-community detection approach for community detection in social networks

Studying social influence in networks is crucial to understand how behavior spreads. An interesting number of theories were elaborated to analyze how innovations and trends get adopted. The traditional view assumes that a minority of members in a society possess qualities that make them exceptionally persuasive in spreading ideas to others. These exceptional individuals drive trends on behalf of the majority of ordinary people. They are loosely described as being informed, respected, and well connected. The leaders or influential are responsible for the dissemination of information and the propagation of influence. In this paper, we propose a new scalable and a deterministic approach for the detection of communities using leaders nodes named Leader-Community Detection Approach LCDA. The proposed approach has two main steps. The first step is the leaders’ retrieval. The second step is the community detection using similarity between nodes. Our algorithms provide good results compared to ground truth membership community.     

Socio-material network analysis: A mixed method study of five European artistic collectives

In this paper, I argue that we can better understand the relationship between social structure and materiality by combining qualitative analysis of practices in shared physical space with statistical analysis. Drawing on the two-mode approach, I treat social and material structures together with the relationship between them as a two-level socio-material network. In a mixed method study, formalized ethnographic data on such networks in five European artistic collectives are subjected to multilevel exponential random graph modelling. It sheds light on how different kinds of interpersonal ties condition the engagement of individuals with similar materiality over time.     

On the Addition and Comparison of Graphs Labeled with Stochastic Variables: Learnable Anatomical Catalogs

We provide an operator for the addition of a pair of graphs, labeled with continuous variables which are subject to stochastic variation. We also provide an operator for measuring dissimilarity between a pair of such graphs. We use such a representation and operators to model a collection of vascular anatomy which accounts for inter-individual variations in both branching structure and in vessel shape. The model may be incrementally acquired, and is thus a catalog of anatomy whose content may be learned. The model may be used in applications such as the reconstruction of vasculature in three-dimensions from x-ray images, which we briefly outline.     

一类循环图的计数(英文)

讨论了 ４度循环图的结构。所有ｎ阶４度非同构的连通循环图被分为两类： Ⅰ类和Ⅱ类。给出了计算Ⅰ类循环图的计算公式,改进了关于ｎ阶４度非同构的循环图的计数公式。     

说“圆”

圆形是最基本的几何图形之一 ,在各种艺术形式中都占据着特殊的地位。文章从形态构成角度出发 ,结合建筑形态的实例对以圆为母题的建筑造型与空间进行了分类研究 ,力图使建筑创作领域的研究更加多元化。     

An introduction to exponential random graph (p) models for social networks

This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov random graph models of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponential random graph models. Sociological Methodology, in press] offer substantial improvement.