Оглавление
Song Yang. Social Network Analysis
Social Network Analysis
Quantitative Applications in the Social Sciences
Social Network Analysis
Contents
Series Editor’s Introduction
About the Authors
Acknowledgments
Chapter 1 Introduction to Social Network Analysis
Chapter 2 Network Fundamentals
2.1 Underlying Assumptions
2.2 Entities and Relations
2.3 Networks
2.4 Research Design Elements
Chapter 3 Data Collection
3.1. Boundary Specification
3.2. Data Collection Procedures
3.3. Cognitive Social Structure
3.4. Missing Data
3.5. Measurement Error
3.6. Collecting Network Data
Chapter 4 Basic Methods for Analyzing Networks
4.1. Network Representation: Graphs and Matrices
4.2. Nodes: Centrality, Power, Prestige
4.3. Dyads: Walk, Path, Distance, Reachability
4.4. Subgroups: Transitivity and Cliques
4.5. Whole Networks: Size, Density, Centralization
4.6. Structural, Regular, and Automorphic Equivalence
Descriptions of Images and Figures
Chapter 5 Advanced Methods for Analyzing Networks
5.1. Ego-Nets
5.2. Visualizations: Clustering, MDS, Blockmodels
5.3. Two-Mode and 3-Mode Networks
Two-Mode Networks
Three-Mode Networks
5.4. Community Detection
5.5. Exponential Random Graph Models (ERGMs)
5.6. Future Directions in Network Analysis
Descriptions of Images and Figures
Appendix Social Network Analysis Software Packages
References
Index
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Social Settings. The first steps in designing a network study are to choose the most relevant social setting and to decide which entities in that setting comprise the network entities. Ordered on a roughly increasing scale of size and complexity, a half-dozen basic units from which samples may be drawn include individual persons, groups (both formal and informal), complex formal organizations, classes and strata, communities, and nation-states. Some two-stage research designs involve a higher-level system within which lower-level entities comprise the actors. Common examples are hierarchical social settings such as corporations with employees, schools with pupils, hospitals with physicians, municipal agencies with civil servants, and universities with colleges with departments with professors.
The earliest and still most common network projects select small-scale social settings—classrooms, offices, factories, gangs, social clubs, schools, villages, artificially created laboratory groups—and treat their individual members as the actors whose relations comprise the networks for investigations. Recent examples include bullying and homophobic teasing among middle school students (Merrin, De La Haye, Espelage, Ewing, Tucker, Hoover, & Green, 2018), helping and gossip networks among employees of a Turkish retail clothing company (Erdogan, Bauer, & Walter, 2015), and the effects of ethnic diversity on the spread of word-of-mouth information in two matched rural Ugandan villages (Larson & Lewis, 2017). Small settings have considerable advantages in sharply delineated membership boundaries, completely identified populations, and usually researcher access by permission from a top authority. However, network analysis concepts and methods are readily applied to larger-scale formations, many of which have porous and fuzzy boundaries, including clandestine networks. Examples include peer network origins of adolescent dating behavior (Kreager, Molloy, Moody, & Feinberg, 2016), criminal organizations in communities of Calabria, Italy (Calderoni, Brunetto, & Piccardi, 2017), and strategic alliances among multinational corporations in the Global Information Sector (Knoke, 2009).
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