Understanding Social Networks: Theories, Concepts, and Findings

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Despite the swift spread of social network concepts and their applications and the rising use of network analysis in social science, there is no book that provides a thorough general introduction for the serious reader. Understanding Social Networks fills that gap by explaining the big ideas that underlie the social network phenomenon. Written for those interested in this fast moving area but who are not mathematically inclined, it covers fundamental concepts, then discusses networks and their core themes in increasing order of complexity. Kadushin demystifies the concepts, theories, and findings developed by network experts. He selects material that serves as basic building blocks and examples of best practices that will allow the reader to understand and evaluate new developments as they emerge. Understanding Social Networks will be useful to social scientists who encounter social network research in their reading, students new to the network field, as well as managers, marketers, and others who constantly encounter social networks in their work.

Exponential Random Graph Models for Social Networks (Structural Analysis in the Social Sciences)

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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.