Social network analysis : history, theory & methodology
著者
書誌事項
Social network analysis : history, theory & methodology
SAGE, 2012
- : hbk
- : pbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
We live in a world that is paradoxically both 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 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 clear 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.
目次
Introduction: What Are Social Networks?
PART ONE: BACKGROUND UNDERSTANDING
Becoming Familiar with Social Networks
A Brief History of Social Network Analysis
How to Study Social Networks, from Theory to Design
PART TWO: LEVELS OF ANALYSIS
Actor Level in Complete Networks
Actor Level in Ego Networks
Dyad and Triad Levels
Subgroups Level
Network Level
Position and Roles Analysis
PART THREE: Advances, Extensions and Conclusions
Statistical Models for Social Networks
Conclusions and Future of the Field
Appendices
Appendix 1: Getting Started with UCINET
Appendix 2: Exponential Random Graph Modelling and Longitudinal Modelling through Siena
Appendix 3: Calculating Probability for Transitivity Using Actor-Based Models
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