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Written by a stellar team of experts, Analyzing Social Networks is a practical book on how to collect, visualize, analyze and interpret social network data with a particular emphasis on the use of the software tools UCINET and Netdraw.
The book includes a clear and detailed introduction to the fundamental concepts of network analyses, including centrality, subgroups, equivalence and network structure, as well as cross-cutting chapters that helpfully show how to apply network concepts to different kinds of networks.
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This sparkling Handbook offers an unrivalled resource for those engaged in the cutting edge field of social network analysis. Systematically, it introduces readers to the key concepts, substantive topics, central methods and prime debates. Among the specific areas covered are: o Network theory . o Interdisciplinary applications. o Online networks. o Corporate networks. o Lobbying networks. o Deviant networks. o Measuring devices. o Key Methodologies. o Software applications. The result is a peerless resource for teachers and students which offers a critical survey of the origins, basic issues and major debates. The Handbook provides a one-stop guide that will be used by readers for decades to come.
Some of the most important international security threats stem from terror groups, criminal enterprises, and other violent non-state actors (VNSAs). Because these groups are often structured as complex, dark networks, analysts have begun to use network science to study them. However, standard network tools were originally developed to examine companies, friendship groups, and other transparent networks. The inherently clandestine nature of dark networks dictates that conventional analytical tools do not always apply. Data on dark networks is incomplete, inaccurate, and often just difficult to find. Moreover, dark networks are often organized to undertake fundamentally different tasks than transparent networks, so resources and information may follow different paths through these two types of organizations. Given the distinctive characteristics of dark networks, unique tools and methods are needed to understand these structures. Illuminating Dark Networks explores the state of the art in methods to study and understand dark networks.
Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages and interpret the results.