The leading guidebook for social network students and researchers, particularly those using NetDraw and UCINET data analysis software, now with updated tools, methods and statistical models.
The leading guidebook for social network students and researchers, particularly those using NetDraw and UCINET data analysis software, now with updated tools, methods and statistical models.
Stephen P. Borgatti, PhD is the Gatton Endowed Chair of Management at the Gatton College of Business and Economics at the University of Kentucky. He has published extensively in management journals, as well as cross-disciplinary journals such as Science and Social Networks. He has published over 100 peer-reviewed articles on network analysis, garnering more than 80,000 Google Scholar citations. With Martin Everett, Steve is co-author of UCINET, a well-known software package for social network analysis, as well as founder of the annual LINKS Center workshop on social network analysis. He is also a two-term past president of INSNA (the professional association for network researchers) and winner of their Simmel Award for lifetime achievement.
Inhaltsangabe
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
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
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