Analyzing Social Networks

Designed to walk beginners through core aspects of collecting, visualizing, analyzing, and interpreting social network data, this book will get you up-to-speed on the theory and skills you need to conduct social network analysis. Using simple language and equations, the authors provide expert, clear insight into every step of the research process-including basic maths principles-without making assumptions about what you know. With a particular focus on NetDraw and UCINET, the book introduces relevant software tools step-by-step in an easy to follow way.

Chapter 1: Introduction Why networks? What are networks? Types of relations Goals of analysis Network variables as explanatory variables Network variables as outcome variablesChapter 2: Mathematical Foundations Graphs Paths and components Adjacency matrices Ways and modes Matrix productsChapter 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 considerationsChapter 4: Data Collection Network questions Question formats Interviewee burden Data collection and reliability Archival data collection Data from electronic sourcesChapter 5: Data Management Data import Cleaning network data Data transformation Normalization Cognitive social structure data Matching attributes and networks Converting attributes to matrices Data exportChapter 6: Multivariate Techniques Used in Network Analysis Multidimensional scaling Correspondence analysis Hierarchical clusteringChapter 7: Visualization Layout Embedding node attributes Node filtering Ego networks Embedding tie characteristics Visualizing network change Exporting visualizations Closing commentsChapter 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 indicesChapter 10: Centrality Basic concept Undirected, non-valued networks Directed, non-valued networks Valued networks Negative tie networksChapter 11: Subgroups Cliques Girvan-Newman algorithm Factions and modularity optimization Directed and valued data Computational considerations Performing a cohesive subgraph analysis Supplementary materialChapter 12: Equivalence Structural equivalence Profile similarity Blockmodels The direct method Regular equivalence The REGE algorithm Core-periphery modelsChapter 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 EquivalenceChapter 14: Large Networks Reducing the size of the problem Choosing appropriate methods Sampling Small-world and scale-free networksChapter 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