In This Article Social Networks

  • Introduction
  • Textbooks and Handbooks
  • Journals
  • Network Datasets
  • Professional Associations and Workshops
  • Kin, Friends, Community
  • Social Capital
  • Intraorganizational Networks
  • Organizational Learning and Innovation Diffusion
  • Interorganizational Relations
  • Politcal Networks
  • International Relations
  • Big Networks, Small Worlds

Sociology Social Networks
by
David Knoke
  • LAST MODIFIED: 27 July 2011
  • DOI: 10.1093/obo/9780199756384-0051

Introduction

Social network analysis comprise theories and methods of investigating structural relations among social actors and explaining social outcomes as the result of connections at the individual, subgroup, and complete network levels of analysis. Originating in social psychology, small group studies, and anthropology in the middle of the 20th century, the network perspective blossomed in the 1970s through a convergence between theoretical interests in structural sociology and proliferating computer programs capable of analyzing network data using matrix algebra techniques. The numbers of publications, research projects, and academic programs grew exponentially in the decades since the 1970s as virtually all basic and applied social science disciplines discovered the relevance of structural relations to their intellectual concerns. The micro-level foundations of social networks are concerned with people choosing to interact with one another in various ways, from forming friendships and exchanging information, to giving advice and assistance, to political and sexual relations. Such small-scale decisions aggregate to more meso-level social structures that can hinder or facilitate collective action by groups and organizations, such as athletic team performance or work group productivity. At the most macro-levels of analysis, the structures and actions of national economies and international systems of sovereign nations can be explained with the conceptual and empirical tools provided by social network analysis. This online bibliography takes a predominantly historical approach to the development of social network analysis, emphasizing the key theoretical, methodological, and substantive publications with which most aspiring networkers should familiarize themselves.

Textbooks and Handbooks

Because social network analysis is rarely taught to undergraduates, no comprehensive textbook has tackled theoretical issues and substantive applications at that level. A few general handbooks or readers on social networks topics have been published, but many become outdated in this rapidly developing interdisciplinary field. At the graduate level, network methods are typically an integral or dominant part of an introductory course. Many methods texts either require or teach matrix algebra and graph theory as foundations for introducing relational data concepts and measures of network structure, such as sociograms, density, centrality, cliques, social cohesion, structural equivalence, blockmodels, or more specialized and advanced topics. Two general introductions to social network analysis, suitable for novices having scant mathematical preparation, are Scott 2000 and Knoke and Yang 2008. Wasserman and Faust 1994 and Jackson 2008 are advanced texts requiring readers to become familiar with matrix algebra. Two recent collections featuring specialized topics are Carrington, et al. 2005 and Carrington and Scott 2011, while de Nooy, et al. 2005 is a user’s guide to a popular computer program for visualization of networks.

  • Carrington, Peter, and John Scott, eds. The SAGE handbook of social network analysis. 2011. Thousand Oaks, CA: SAGE.

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    With nearly forty chapters by prominent network analysts, this collection spans a broad range of contemporary theoretical, methodological, and substantive issues, including social capital; diffusion; kinship; online networks; social movements; policy; and terrorist, scientific, and world systems networks.

  • Carrington, Peter J., John Scott, and Stanley Wasserman, eds. 2005. Models and methods in social network analysis. New York: Cambridge Univ. Press.

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    Thirteen chapters present accessible introductions to recent developments and advanced network methods, including network sampling, blockmodeling, correspondence analysis, diffusion, expected random graph models (ERGMs), and models for studying change with longitudinal network data.

  • de Nooy, Wouter, Andrej Mrvar, and Vladimir Batagelj. 2005. Exploratory social network analysis with Pajek. New York: Cambridge Univ. Press.

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    After covering basic social network analysis concepts, demonstrates how to use the Pajek computer package to perform cohesion, brokerage, ranking, and blockmodel analyses. Special strengths are many examples, detailed computer instructions, and exercises with examples.

  • Jackson, Matthew O. 2008. Social and economic networks. Princeton, NJ: Princeton Univ. Press.

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    A comprehensive monograph aimed at economists, who have been discovering the prevalence of networks in markets and other economic actions. Although this book overlaps with sociological approaches, Jackson offers a game-theory perspective missing in that discipline. The notation system is quite formidable, and network novices should not begin their studies with this volume.

  • Knoke, David, and Song Yang. 2008. Social network analysis. 2d ed. Los Angeles: SAGE.

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    A complete revision of a network analysis primer that treats basic methods in some detail and sketches more advanced topics. A distinctive feature is attention to issues of network measurement and data collection. Originally published in 1982.

  • Scott, John. 2000. Social network analysis: A handbook. 2d ed. London: SAGE.

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    A more elementary treatment of methods, not reliant on formal notation, mixed with brief histories of network research from classical to contemporary applications.

  • Wasserman, Stanley, and Katherine Faust. 1994. Social network analysis: Methods and applications. New York: Cambridge Univ. Press.

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    A lengthy, comprehensive treatment of both elementary and advanced topics in network data analysis, with numerous illustrations on some classic small datasets. Emphasizes both graphs and algebraic formulas. The latter initially requires persistent effort to learn the standardized notation, but pays off with many insights into structural relations.

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