Borgatti, Stephen 　著

内容

目次

Chapter 1: Introduction Why networks? What are networks? Types of relations Goals of analysis Network variables as explanatory variables Network variables as outcome variables 1.7 ConclusionChapter 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 matricesChapter 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 visualizationsChapter 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