Social Network Analysis History, Theory and Methodology
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Description: We live in a world that is paradoxically small and vast: each of us is embedded in local communities and yet we are only a few 'links' away from anyone else in the world. This engaging book represents these interdependencies' positive and negative consequences, their multiple effects and the ways in which a local occurrence in one part of the world can directly affect the rest. Then it demonstrates precisely how these interactions and relationships form. This is a book for the social network novice on learning how to study, think about and analyse social networks; the intermediate user, not yet familiar with some of the newer developments in the field; and the teacher looking for a range of exercises, as well as an up-to-date historical account of the field. It is divided into three sections: 1. Historical & Background Concepts 2. Levels of Analysis 3. Advances, Extensions and Conclusions The book provides a full overview of the field - historical origins, common theoretical perspectives and frameworks; traditional and current analytical procedures and fundamental mathematical equations needed to get a foothold in the field.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
Copyright year: 2011
Publisher: SAGE Publications, Limited
Publication date: 10/26/2011
Size: 6.50" wide x 9.25" long x 0.75" tall
|Introduction: What Are Social Networks?|
|Becoming Familiar with Social Networks|
|a Brief History of Social Network Analysis|
|How to Study Social Networks, from Theory to Design.|
|Levels of Analysis|
|Actor Level in Complete Networks|
|Actor Level in Ego Networks|
|Dyad and Triad Levels|
|Position and Roles Analysis|
|Advances, Extensions and Conclusions|
|Statistical Models for Social Networks|
|Conclusions and Future of the Field|
|Getting started with UCINET|
|Exponential Random Graph Modelling and Longitudinal Modelling through Siena|
|Calculating Probability for Transitivity Using Actor-Based Models|