Analyzing Social Networks

Chapter 1: Introduction
Why networks?
What are networks?
Types of relations
Goals of analysis
Network variables as explanatory variables
Network variables as outcome variables

Chapter 2: Mathematical Foundations
Graphs
Paths and components
Adjacency matrices
Ways and modes
Matrix products

Chapter 3: Research Design
Experiments and field studies
Whole-network and personal-network research designs
Sources of network data
Types of nodes and types of ties
Actor attributes
Sampling and bounding
Sources of data reliability and validity issues
Ethical considerations

Chapter 4: Data Collection
Network questions
Question formats
Interviewee burden
Data collection and reliability
Archival data collection
Data from electronic sources

Chapter 5: Data Management
Data import
Cleaning network data
Data transformation
Normalization
Cognitive social structure data
Matching attributes and networks
Converting attributes to matrices
Data export

Chapter 6: Multivariate Techniques Used in Network Analysis
Multidimensional scaling
Correspondence analysis
Hierarchical clustering

Chapter 7: Visualization
Layout
Embedding node attributes
Node filtering
Ego networks
Embedding tie characteristics
Visualizing network change
Exporting visualizations
Closing comments

Chapter 8: Testing Hypotheses
Permutation tests
Dyadic hypotheses
Mixed dyadic–monadic hypotheses
Node level hypotheses
Whole-network hypotheses
Exponential random graph models
Stochastic actor-oriented models (SAOMs)

Chapter 9: Characterizing Whole Networks
Cohesion
Reciprocity
Transitivity and the clustering coefficient
Triad census
Centralization and core–periphery indices

Chapter 10: Centrality
Basic concept
Undirected, non-valued networks
Directed, non-valued networks
Valued networks
Negative tie networks

Chapter 11: Subgroups
Cliques
Girvan–Newman algorithm
Factions and modularity optimization
Directed and valued data
Computational considerations
Performing a cohesive subgraph analysis
Supplementary material

Chapter 12: Equivalence
Structural equivalence
Profile similarity
Blockmodels
The direct method
Regular equivalence
The REGE algorithm
Core–periphery models

Chapter 13: Analyzing Two-mode Data
Converting to one-mode data
Converting valued two-mode matrices to one-mode
Bipartite networks
Cohesive subgroups and community detection
Core–periphery models
Equivalence

Chapter 14: Large Networks
Reducing the size of the problem
Choosing appropriate methods
Sampling
Small-world and scale-free networks

Chapter 15: Ego Networks
Personal-network data collection
Analyzing ego network data
Example 1 of an ego network study
Example 2 of an ego network study