Suchergebnisse

Discusses the value of social network analysis for teasing out the network processes in social movements, particularly (1) how collective action is affected by actors' embeddedness in preexisting networks, & (2) how actors create new linkages that shape the subsequent development of protest &/or subcultural activities. Issues of data collection & organization are examined in detail, & ways of analyzing social movement structure & individual positions in social movement networks are demonstrated. A combination of qualitative & quantitative methods is deemed the most useful in network analysis. 5 Tables, 3 Figures, 74 References. K. Hyatt Stewart

David Knoke and Song Yang's Social Network Analysis, Third Edition provides a concise introduction to the concepts and tools of social network analysis. The authors convey key material while at the same time minimizing technical complexities. The examples are simple: sets of 5 or 6 entities such as individuals, positions in a hierarchy, political offices, and nation-states, and the relations between them include friendship, communication, supervision, donations, and trade. The new edition reflects developments and changes in practice over the past decade. The authors also describe important recent developments in network analysis, especially in the fifth chapter. Exponential random graph models (ERGMs) are a prime example: when the second edition was published, P* models were the recommended approach for this, but they have been replaced by ERGMs. Finally, throughout the volume, the authors comment on the challenges and opportunities offered by internet and social media data.

2011 IEEE Global Engineering Education Conference, EDUCON 2011, Amman, 4 - 6 April 2011, 606-611 ; Alumni connections are important resources that contribute to university evaluation. Even though alumni connections represent networks, they have been mostly evaluated as tabular data (e.g. by providing average salary, employment rate, etc.). This ironically disregards all qualities of a network, from which an alumni network gets its name. It is desirable to evaluate an alumni network as a network, because networks have the potential to provide very insightful information. Evaluation of alumni networks as a network has not been feasible in the past due to data fragmentation (neither universities nor companies willing to share meaningfully significant data in its entirety). Recently the feasibility of such analysis has changed, due to new trends towards democratization of information, accelerated by the Web 2.0 user-generated content phenomenon and crowd-sourcing mentality. Utilizing web-crawlers, we actively harvested data and assembled a dataset on alumni in leadership positions in technology-based industries. Moreover, we included a high proportion of startup companies, which allowed us to evaluate alumni networks with respect to entrepreneurial as well as technology involvement. We show that by analyzing alumni connections as networks, it is possible to uncover new patterns, as well as provide a new way of examining the old

This paper reports on the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work. It is argued that the concept of social network provides a powerful model for social structure, and that a number of important formal methods of social network analysis can be discerned. Social network analysis has been used in studies of kinship structure, social mobility, science citations, contacts among members of deviant groups, corporate power, international trade exploitation, class structure, and many other areas. A review of the formal models proposed in graph theory, multidimensional scaling, and algebraic topology is followed by extended illustrations of social network analysis in the study of community structure and interlocking directorships.

Fueled by recent advances in statistical modeling and the rapid growth of network data, social network analysis has become increasingly popular in sociology and related disciplines. However, a significant amount of work in the field has been descriptive and correlational, which prevents the findings from being more rigorously translated into practices and policies. This article provides a review of the popular models and methods for causal network analysis, with a focus on causal inference threats (such as measurement error, missing data, network endogeneity, contextual confounding, simultaneity, and collinearity) and potential solutions (such as instrumental variables, specialized experiments, and leveraging longitudinal data). It covers major models and methods for both network formation and network effects and for both sociocentric networks and egocentric networks. Lastly, this review also discusses future directions for causal network analysis.

Understanding relationships is fundamental to marketing. Research has moved beyond simple dyadic relations to examine how networks of relations influence behaviour. While network theory is frequently drawn upon in marketing, few researchers apply the formal network analytical techniques developed. The aim here is to make network analysis more accessible. In this paper we discuss the basic data requirements and use a large business-to-business communication network to demonstrate a number of network measures and theories that have been applied in marketing studies.

This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses