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Analyzing social networks / Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson

Borgatti, Stephen P.
Contributor(s): Everett, Martin G. ( Martin George) (, 1955-) | Johnson, Jeffrey C. ( Jeffrey Carl).
Material type: materialTypeLabelBook; Format: print Publisher: London : SAGE Publications, 2018Edition: 2 ed.Description: , p. : il. ; 24 cm.ISBN: 9781526404107 (pbk); 1526404109.Subject(s): Redes sociales
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Item type Home library Call number Status Loan Date due Barcode Item holds
Monografías 07. BIBLIOTECA CIENCIAS SOCIALES
658.8/BOR/ana (Browse shelf) Checked out PREST. LIBROS 15/03/2019 3744568370
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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

Esta es una guía indispensable para los investigadores en la recopilación, análisis e interpretación de datos de redes sociales. - Garry Robins Un excelente libro para estudiantes y académicos establecidos que quieren participar seriamente en el análisis de las redes sociales. Los autores proporcionan una excelente introducción al campo, pero también ofrecen la profundidad que permite al lector realizar análisis de vanguardia. Cada capítulo viene con ejercicios y resultados de aprendizaje claramente definidos, lo que me hace recomendar este libro a todos mis alumnos. Es uno de los mejores libros sobre análisis de redes sociales que he visto hasta ahora. - Thomas Grund

What do rumours, viruses and global trade have in common? They are all transmitted through a network. For some, this is the start of thinking how all networks share similar properties. For me, such platitudes are getting passe; of course networks are everywhere! Finally, this book goes beyond superficial commonalities in networks to provide a coherent framework for the many different kinds of social networks available to the researcher. The authors help us understand which differences matter, how to analyse them and how to make sense of the results. These days its easy to be sold on the power of network analysis, but it is much harder to know which analysis to do and why. Thankfully, Borgatti, Everett and Johnson have given us a text that is as conceptually rich as it is methodologically generous. -- Bernie Hogan The first edition of this book was a winner ... and this edition is even better. The clear writing, the new glossary at the end of the book, and the exercises at the end of each chapter make this edition a wonderful book to teach from. Highly recommended. -- H. Russell Bernard Other books are about social networks. Look here for the best introduction to doing network research. If you want to learn to design a network study, analyze networks, and test hypotheses about social connectivity, this is the book for you. -- Ronald Breiger The first edition of this fine text has quickly become a leading resource for the conduct of social network research and the analysis of social network data, especially for those researchers using the UCINET software to analyse data. So it is especially valuable to see an updated second edition appearing. This is an indispensable guide for researchers in the collection, analysis and interpretation of social network data. -- Garry Robins An excellent book for students and established scholars alike who want to seriously get into the analysis of social networks. The authors provide a superb introduction to the field, but also offer the depth that enables the reader to perform state-of-the-art analyses. Each chapter comes with clearly defined learning outcomes and exercises, which makes me recommend this book to all my students. It is one of the best books on the analysis of social networks that I have seen so far. -- Thomas Grund

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