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Communication Communication Networks
by
Peter Monge, Drew Margolin

Preparation of this article was supported in part by grants from the National Science Foundation (IIS-0838548) and by support provided to the Annenberg Networks Network by the Annenberg School for Communication and Journalism.

Introduction

Network scholarship has grown substantially in the first decade of the 21st century across a wide spectrum of the academy. Network theory, concepts, tools, and techniques have become increasingly central to work in such diverse fields as biology, communication, physics, political science, and economics, to name but a few. This growth has been fostered by a recognition that many phenomena in the social and physical world that have traditionally been studied in isolation are in reality interconnected: in short, linking matters. This recognition has fostered renewed efforts to develop network theory, expand the scope of network research, and connect phenomena across disciplines. These efforts have been aided by at least three important trends. The first is the increased connectivity in social relationships provided by the Internet, mobile phones, and other new communication technologies. The second is a greater ability to gather and analyze large communication and other social data sets. And, the third is the improved computational power and higher level of analytical sophistication provided by new network computer programs. The working bibliography that follows provides an overview of this rapidly growing and changing field, with a focus on theory, method, and selected research topics.

General Overviews

Network concepts operate at many different levels (individuals, pairs, small groups, large groups) and are often defined by what can be measured or calculated. The texts that follow provide useful entry points into this diverse domain. Wasserman and Faust 1994 details the basic conceptual and methodological building blocks of network analysis. It is the most comprehensive network text and the standard reference for social network analysis. Monge and Contractor 2003 provides a history and synthesis of the theoretical approaches to network research. Easley and Kleinberg 2010 demonstrates how a variety of anomalous and counterintuitive results from the point of view of classic, rational models of behavior can be modeled or explained with network approaches. Barabási 2003 offers a popular introduction to the thinking of large populations in terms of their relational structure. Lazer, et al. 2009 suggests the kinds of questions and challenges that social scientists can address in the future with computational approaches; in particular, network approaches.

  • Barabási, Albert-László. 2003. Linked: How everything is connected to everything else and what it means for business, science, and everyday life. New York: Plume.

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    Written for a popular audience and therefore does not offer much in the way of theoretical or methodological detail; however, this is a good introduction to the ways in which network scientists approach research problems.

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  • Easley, David, and Jon Kleinberg. 2010. Networks, crowds, and markets: Reasoning about a highly connected world. New York: Cambridge Univ. Press.

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    Explores the intersection of theories and findings from network science with rational models of human behavior. Substantial attention is given to game theory and rational expectations–based models. The text also includes useful, detailed explanations of such concepts as information cascades, small worlds, and the PageRank algorithm, central to the success of online search engines.

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  • Lazer, David, Alex Pentland, Lada Adamic, et al. 2009. Life in the network: The coming age of computational social science. Science 323.5915: 721–723.

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    Coauthored by leading researchers working in the intersection of networks and social science. They argue that network analysis will be at the center of this transformation and also articulate some of the important research questions and challenges that should be addressed by the next generation of social scientists.

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  • Monge, Peter R., and Noshir S. Contractor. 2003. Theories of communication networks. Oxford and New York: Oxford Univ. Press.

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    Monge and Contractor provide a comprehensive overview and analysis of the various theoretical approaches that have been applied by social scientists to social network phenomena. Theoretical approaches are grouped into families with common rationales or similar predictions. The authors also propose and describe the multitheoretical, multilevel (MTML) approach, in which complex network phenomena are arranged into subcomponents that may operate by different theoretical rationales.

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  • Wasserman, Stanley, and Katherine Faust. 1994. Social network analysis: Methods and applications. Structural Analysis in the Social Sciences. Cambridge, UK, and New York: Cambridge Univ. Press.

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    The classic, comprehensive text describing the application of network methods in detail. Particularly useful for its extensive explication of network concepts and measures, such as the different forms of centrality. Unlike most texts focused on network methods, Wasserman and Faust is not tied to any particular software application or family of research questions.

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Journals

Although research using network-related theories and methods is expanding, there are still very few journals specializing in this area. Social Networks, the flagship journal of the International Network for Social Network Analysis (INSNA), is the most well-known journal devoted to network research. INSNA also publishes Connections and the Journal of Social Structure, an online-only electronic journal. Communication and other social network research also feature prominently in work published in many of the most prominent social science journals, particularly in sociology. American Journal of Sociology has published many of the most influential articles regarding network theory and its application to social science research questions. Administrative Science Quarterly has published a number of articles that focus on both intraorganizational communication networks and strategic alliances. Within communication, the publication of network-related research is growing. The Journal of Communication leads the way, having published a number of articles in this area over the last several years. It is common knowledge that the study of human communication is transdisciplinary; consequently, it should come as no surprise that theoretical and research articles on human communication are scattered widely throughout the scholarly literature and are in no way limited to the social sciences. The list of journals that publish scholarly work in this area seems endless. Accordingly, the best way to keep up with research on communication networks is to cast a wide net.

Theories

Network research is often criticized for being analytical rather than theoretical. Nonetheless, at the core of network research are important theoretical concepts around which modest theories have been developed. Monge and Contractor 2003 reviews major social science theories to explicate the network implications of each. Borgatti and Foster 2003 forms a typology of network theories based on distinctions between network links as a means of providing resources, information, and a network as social structure that enables and constrains behavior through the construction and manipulation of rules and identities. The former approach is exemplified by the theory of “weak ties” (Granovetter 1973) and the theory of structural holes (Burt 1995). Granovetter’s weak ties provide advantages to individuals by giving access to novel information and ideas. Burt argues that individuals can actively obtain these advantages by seeking out opportunities to build weak ties between unconnected others (structural holes). Approaches to power (Castells 2009, Grewal 2008) are social-structural approaches. Castells argues that power is embedded in communication relationships; individuals can use their position within these relationships in order to influence not only resource flows, but also rules and identities. Grewal 2008 focuses on networks as the means of coordinating collective standards that govern behavior.

  • Borgatti, Stephen P., and Pacey C. Foster. 2003. The network paradigm in organizational research: A review and typology. Journal of Management 29.6: 991–1013.

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    This article reviews network research from a conceptual point of view. The authors make the distinction between structural and connectionist approaches to network influence and social capital and diffusion approaches to network outcomes based on the kinds of variables chosen as independent and dependent. The conceptual distinctions are helpful as network researchers, particularly in social capital research, and often deploy concepts and measurements in different ways.

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  • Burt, Ronald S. 1995. Structural holes: The social structure of competition. Cambridge, MA: Harvard Univ. Press.

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    Burt articulates the reasons and mechanisms by which individuals might seek to obtain and exploit the benefit of weak ties (see Granovetter 1973) by identifying the potential to form ties between otherwise unconnected individuals. This work calls attention to the idea of networks as active, strategic creations of individuals rather than simply as given environments that individuals accept.

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  • Castells, Manuel. 2009. Communication power. Oxford and New York: Oxford Univ. Press.

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    Castells tackles questions related to the concept of power within the network society. The text is an excellent bridge between critical theory’s abstract concerns with power and domination and the concrete manifestations of power in the network society in areas such as financial services and media conglomeration.

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  • Granovetter, Mark S. 1973. The strength of weak ties. American Journal of Sociology 78.6: 1360–1380.

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    Included in the small canon of articles that network researchers are familiar with. Granovetter introduces the idea of weak ties—relationships between individuals of moderate intensity—and proposes that these ties provide the benefit of novel information. This argument is extended and supported by theories of brokerage; in particular see Burt 2005 (cited under Organizations).

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  • Grewal, David Singh. 2008. Network power: The social dynamics of globalization. New Haven, CT. Yale Univ. Press.

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    Grewal argues that networks create dynamics of power because of their ability to coordinate standards. These standards grow in importance as more individuals join the network, thus undermining the opportunities for alternatives. In addition to a cogent theoretical argument, the book offers interesting historical examples of network power in action.

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  • Monge, Peter R., and Noshir S. Contractor. 2003. Theories of communication networks. Oxford and New York: Oxford Univ. Press.

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    Network research is often criticized for being atheoretical. Monge and Contractor shows how various predictions regarding the formation and influence of social network structure are implicitly based on social-theoretic perspectives from psychology, sociology, biology, and other fields.

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Macro-Social Theory

Several scholars have used an emphasis on networks to rethink classic definitions and assumptions regarding macro-social phenomena and policy in areas such as science, economics, and warfare. Although the analyses and recommendations differ, these arguments share a common skepticism toward classic models that reify the autonomy of individuated entities: individual people (Castells 2000), individual nation-states (Arquilla and Ronfeldt 2001), individual societies (Latour 2005), and individual claimants to property (Benkler 2006). Castells 2000 describes the major social and economic transformations since the 1980s in terms of the emergence of overlapping networks of social interaction. Latour 2005 suggests ways in which these overlapping networks, observable and recordable for the first time in history, demand a new kind of attention from social scientists. Arquilla and Ronfeldt 2001 looks at the darker side of the transformation. The authors presciently argue (prior to the attacks of 9/11) that the emergence of Internet and mobile communication technology transforms the task of successfully deploying military force and suggest that this change shifts the balance of power toward small, networked adversaries of large nation-states, such as the United States. Benkler 2006 similarly argues that organization via networks provides advantages to the “little guy” in the economic realm, especially in the areas of intellectual and creative work. Berry 2008 provides a useful critique of the overreliance on network concepts to explain emerging social phenomena.

  • Arquilla, John, and David Ronfeldt. 2001. Networks and netwars: The future of terror, crime, and militancy. Santa Monica, CA: Rand.

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    The authors argue that the future of warfare and combat will be between combatants organized in networks rather than in traditional regimented, hierarchical armies. The text is noteworthy for insights regarding the importance of information, communication, and coordination for combat in comparison with the importance of supply lines and material in 20th-century warfare.

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  • Benkler, Yochai. 2006. The wealth of networks: How social production transforms markets and freedom. New Haven, CT: Yale Univ. Press.

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    Benkler examines the impact of new communication technology—in particular the increased capability for virtual and long-distance communication, coproduction, and sharing of intellectual products—on our conceptions of property and markets. Benkler contends that this new form of production makes the classical and neoclassical economic models obsolete. This text is stimulating in its strident yet thoughtful stance on the nature of property, especially intellectual property.

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  • Berry, David. M. 2008. The poverty of networks. Theory Culture and Society 25.7–8: 364–372.

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    A useful critique of network approaches to theory. In a review of three works, including Benkler 2006, the author uncovers assumptions and slippery concepts, in particular the tendency for these theories to depend too heavily on the physical and political economic structures of the Internet and contemporary communication technology.

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  • Castells, Manuel. 2000. The rise of the network society. 2d ed. Information Age 1. Oxford and Malden, MA: Blackwell.

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    Castells reconceives society as an interlocking set of networks through a comprehensive review of the social, political, and economic transformations of the second half of the 20th century. The text smoothly integrates abstract theoretical concepts, such as values and identity, with specific examples from a variety of social phenomena worldwide. This book is the first in a trilogy by Castells; all three are a worthy intellectual investment.

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  • Latour, Bruno. 2005. Reassembling the social: An introduction to actor-network theory. Clarendon Lectures in Management Studies. Oxford and New York: Oxford Univ. Press.

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    Largely a treatise on the history and future direction of social science. Latour argues that scholars should abandon the idea of “society” as a generic aggregate of individuals and focus instead on networks through which individuals are “activated” into observed activities, including the construction of their own identities. Although opaque at times, this book can help expand the imagination.

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Methods

Network research employs a unique set of measures, data collection techniques, and inferential tools. It also must deal with unique data and analytic problems. For example, most social science analytic techniques are based on the assumption that data observations are independent of each other. This assumption is clearly violated in network research, in which linkages connect nodes to each other. Thus, network techniques must address these inherent dependencies in the data. This important shift in assumptions has necessitated the development of a variety of novel research methods.

Measures

A scholar new to network analysis will first notice the unique measures and indexes. Network researchers refer to properties such as centrality, centralization, reciprocity, transitivity, n-cliques, and k-cores, to name a few. The generation of new measures and variants on existing measures has been a tradition in network research since the early days of the field. Often, scholars will introduce new theoretical ideas in conjunction with novel measures designed to capture these ideas. Wasserman and Faust 1994 is the standard introduction to network measures and techniques. This introduction can be supplemented by de Nooy, et al. 2005, which focuses on newer techniques, with additional detail related to visualization techniques. Easley and Kleinberg 2010 explicates measures useful for analysis in studies of the Internet.

  • de Nooy, Wouter, Andrej Mrvar, and Vladimir Batagelj. 2005. Exploratory social network analysis with Pajek. Stuctural Analysis in the Social Sciences. Cambridge, UK, and New York: Cambridge Univ. Press.

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    An introduction to social network analysis combined with tutorials in Pajek, an analysis program. This text emphasizes techniques for use in Pajek (see Software), but it also contains descriptions of measures for large networks (greater than 1,000 nodes), such as k-components. After mastering Wasserman and Faust 1994, this text is a useful supplement.

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  • Easley, David, and Jon Kleinberg. 2010. Networks, crowds, and markets: Reasoning about a highly connected world. New York: Cambridge Univ. Press.

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    Although not explicitly a methodological primer, this text provides the specifications of a variety of network measures, including those used in the analysis of hyperlink networks and other techniques related to analysis of the Internet.

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  • Wasserman, Stanley, and Katherine Faust. 1994. Social network analysis: Methods and applications. Structural Analysis in the Social Sciences. Cambridge, UK, and New York: Cambridge Univ. Press.

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    The classic, definitive text explicating network analytic methods. The book is particularly useful for its comprehensive and mutually consistent specification of network concepts and measurements such as the different forms of centrality. Unlike most texts that emphasize network methods, Wasserman and Faust 1994 is not tied to any particular software application or family of research questions.

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Data Collection

Unfortunately, most of the attention in the development of network methodology has been on novel calculations rather than the development and empirical validation of appropriate data collection techniques. As a result, there has been little research on the sensitivity of different data collection procedures to the precision of these measures. Marsden 2005 describes and reviews literature related to the primary issues in network data collection, such as network boundaries and subject response issues. Valente 2010 provides a practical guide to researchers seeking to negotiate these issues in field research. Borgatti, et al. 2006 confronts the problem of the sensitivity of measures to errant data.

  • Borgatti, Stephen P., Kathleen M. Carley, and David Krackhardt. 2006. On the robustness of centrality measures under conditions of imperfect data. Social Networks 28.2: 124–136.

    DOI: 10.1016/j.socnet.2005.05.001Save Citation »Export Citation »E-mail Citation »

    The authors take distorted samples from a true network by introducing various errors and observing their effect on different measures of centrality.

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  • Marsden, Peter V. 2005. Recent developments in network measurement. In Models and methods in social network analysis. Edited by Peter J. Carrington, John Scott, and Stanley Wasserman, 8–30. Structural Analysis in the Social Sciences. Cambridge, UK, and New York: Cambridge Univ. Press.

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    Reviews the major issues in network data collection and the advantages and disadvantages of techniques designed to address them.

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  • Valente, Thomas W. 2010. Social networks and health: Models, methods, and applications. Oxford and New York: Oxford Univ. Press.

    DOI: 10.1093/acprof:oso/9780195301014.001.0001Save Citation »Export Citation »E-mail Citation »

    This book has many specifications and helpful tips for collecting network data through surveys and interviews. Very useful for any researcher planning to collect network data from individuals or group self-reports (as opposed to constructing network data sets from preexisting, secondary data).

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Computational Techniques and Calculations

The development of computational techniques for network research is almost a research field unto itself. Social scientists have settled on a subset of techniques useful for analyzing social networks and related data. The independence assumptions for basic hypothesis testing using standard techniques are violated in network data. Krackhardt 1987 features the quadratic assignment procedure (QAP) to test the correlation between two network structures against a null hypothesis of “no association.” The detection of communities and the subcommunities from which they are built is an important line of network research. White, et al. 1976 describes block modeling, one of the earliest techniques in this area. Newman and Girvan 2004 reviews and describes other major techniques for detecting higher-level structure. A growing area of research uses observed networks to infer underlying social processes via the use of repeated simulations. There are two major approaches to this line of research: exponential random graph models (ERGMs) and agent-based models (ABMs). ERGMs, examined by Robins, et al. 2007, emphasize the likelihood of a particular tie, given the surrounding ties in its local neighborhood. Shumate and Palazzolo 2010 provides a less formal, more accessible introduction to ERGMs. The ABM approach, outlined by Snijders, et al. 2010, emphasizes the decision-making logics by which nodes choose to add or delete ties. Carrington, et al. 2005 offers formal specifications of these and other novel developments written by experts in each field.

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

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    This book is an update on new network models and methods for scholars already familiar with basic network concepts. The chapters include formal specifications for a variety of new techniques, including sampling models, random graph models, diffusion models, and longitudinal models, written by leaders in the field. Suitable for mathematically inclined scholars, as most chapters treat models in formal terms.

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  • Krackhardt, David. 1987. QAP partialling as a test of spuriousness. Social Networks 9.2: 171–186.

    DOI: 10.1016/0378-8733(87)90012-8Save Citation »Export Citation »E-mail Citation »

    Describes the QAP. This technique is used to determine if there is a correlation between two network structures. Because network ties share nodes, they are not independent observations, and thus traditional measures of association, such as Pearson’s r or Spearman’s rho, are inappropriate.

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  • Newman, M.E.J., and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E 69.2: 026113.1–026113.15

    DOI: 10.1103/PhysRevE.69.026113Save Citation »Export Citation »E-mail Citation »

    Reviews major techniques and introduces an algorithm for detecting “communities”— nodes that are part of the same group—within networks. Community detection has subsequently become a subtopic within network research, particularly in research on large networks.

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  • Robins, Garry, Pip Pattison, Yuval Kalish, and Dean Lusher. 2007. An introduction to exponential random graph (p*) models for social networks. Social Networks 29.2: 173–191.

    DOI: 10.1016/j.socnet.2006.08.002Save Citation »Export Citation »E-mail Citation »

    ERGMs use Monte Carlo techniques to explore a space of parameters that can “explain” the structure of a focal network or networks. Parameters are tendencies toward particular local structures or subgraphs, such as stars or triangles. The authors of this introductory article are leaders in the development of ERGMs and provide the rationale and basic specifications of the family of ERGMs. Available online for purchase.

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  • Shumate, Michelle, and Edward T. Palazzolo. 2010. Exponential random graph (p*) models as a method for social network analysis in communication research. Communication Methods and Measures 4.4: 341–371.

    DOI: 10.1080/19312458.2010.527869Save Citation »Export Citation »E-mail Citation »

    Provides a very clear explanation of ERGMs targeted at communication researchers. ERGM concepts are explained with minimal formalization, and a useful illustrative example is provided.

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  • Snijders, Tom A. B., Gerhard G. van de Bunt, and Christian E. G. Steglich. 2010. Introduction to stochastic actor-based models for network dynamics. Social Networks 32.1: 44–60.

    DOI: 10.1016/j.socnet.2009.02.004Save Citation »Export Citation »E-mail Citation »

    Features a conceptual introduction to the use of actor-based models for the testing of hypotheses regarding the dynamic relationship between network structure and individual attributes. The rationale provided is deployed in the program SIENA (Simulation Investigation for Empirical Network Analysis). Available online for purchase.

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  • White, Harrison C., Scott A. Boorman, and Ronald L. Breiger. 1976. Social structure from multiple networks. I. Blockmodels of roles and positions. American Journal of Sociology 81.4: 730–780.

    DOI: 10.1086/226141Save Citation »Export Citation »E-mail Citation »

    Authors introduce and elaborate on block modeling. Block models partition nodes into collections of structurally equivalent individuals. An advantage of block modeling is its application to multiplex ties of many different types among the same set of nodes. This article not only specifies the logic of the technique, but also explains its use in regard to particular kinds of research questions. Available online for purchase.

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Software

A variety of software packages and tools have been developed in the first decade of the 21st century. The following list comprises the major software tools that are available and used by network researchers. UCINET (Borgatti, et al. 2005, Hanneman and Riddle 2005) is the most popular among social scientists. It provides a relatively smooth graphical user interface and access to all the major network measures and techniques used in the social sciences. Pajek (de Nooy, et al. 2005) is less user-friendly but more robust for analysis of large networks (greater than 1,000 nodes). Pajek also offers advanced visualization techniques favored by scientists analyzing large networks in the natural sciences. C-IKNOW (Cyber-infrastructure for Inquiring Knowledge Networks on the Web) (Contractor, et al. 2011) is a user-friendly program with intuitive interfaces and data storage formats. It is designed for use by researchers or corporate managers. Network Workbench (Börner, et al. 2010) and the software developed in R (Butts 2008) are both open-source network packages that can run advanced or novel algorithms as they are developed. PNet (Wang, et al. 2007) was developed to run exponential random graph models; SIENA (Simulation Investigation for Empirical Network Analysis) (Snijders, et al. 2011) was developed to run agent-based models. Both techniques can now be run in R as well.

  • Borgatti, Steve, Martin Everett, and Lin Freeman. 2005. UCINET 6 for Windows software for social network analysis. Harvard, MA: Analytic Technologies.

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    UCINET is the most popular tool for network analysis within the social sciences. This manual provides basic guidelines and is complimented by Hanneman and Riddle 2005.

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  • Börner, Katy, Weixia Huang, Micah Linnemeier, et al. 2010. Rete-netzwerk-red: Analyzing and visualizing scholarly networks using the Network Workbench Tool. Scientometrics 83.3: 863–876.

    DOI: 10.1007/s11192-009-0149-0Save Citation »Export Citation »E-mail Citation »

    The science of science (sometimes called scientometrics) is a growing field. The availability of a tremendous amount of data through the Web of Science and similar sources presents an enormous opportunity and challenge to researchers. The authors introduce Network Workbench as a tool for storing and analyzing large scientometric data sets.

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  • Butts, Carter T. 2008. Social network analysis with sna. In Special volume: Statistical modeling of social networks with “statnet.” Journal of Statistical Software 24.6.

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    The open computing environment R has become popular among social scientists. Researchers can develop algorithms within R to share with one another. This article introduces social network analysis in R. A set of companion articles can be found in the special issue of the Journal of Statistical Software 24.

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  • de Nooy, Wouter, Andrej Mrvar, and Vladimir Batagelj. 2005. Exploratory social network analysis with Pajek. Structural Analysis in the Social Sciences. Cambridge, UK, and New York: Cambridge Univ. Press.

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    Particularly useful as a primer on how to use Pajek. Pajek is designed to handle analysis and visualization of large networks (greater than 1,000 nodes). It is a primary tool for visualization in research areas, such as scientometrics or web science, in which networks easily exceed the capacity of UCINET (though analysis in R is gaining popularity).

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  • Hanneman, Robert A., and Mark Riddle. 2005. Introduction to social network methods. Riverside: Univ. of California.

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    Combines an introduction to network analysis with tutorials for implementation of analysis in UCINET. This online text is particularly useful as a quick reference for finding how particular measures are calculated in UCINET.

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  • Contractor, Noshir, Guangyao Yao, Yun Huang, Jinling Li, and Hugh Devlin. 2011. C-IKNOW.

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    C-IKNOW permits the collection and analysis of network data in an intuitive, user-friendly environment. C-IKNOW is particularly useful for the collection of communication and other social network data from large organizations via online survey techniques. C-IKNOW can also import data from and export data to most network analysis programs, such as UCINET, Pajek, PNet, and SIENA.

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  • Snijders, Tom A. B., Christian E. G. Steglich, Michael Schweinberger, and Mark Huisman. 2011. SIENA.

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    SIENA (Simulation Investigation for Empirical Network Analysis) uses agent-based modeling to simulate changes in networks and provide information about the individual-level logics by which particular networks may have been formed. Two advantages of SIENA are its facility with multiple-stage longitudinal observations and its ability to treat networks and node attributes as either independent or dependent variables.

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  • Wang, P., Garry Robins, and Pip Pattison. 2007. PNet. Melbourne, Australia: Univ. of Melbourne.

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    This site contains documentation and information on the software application PNet, the primary tool used for exponential random graph modeling (ERGM). Researchers are encouraged to first read Robins, et al. 2007 (cited under Computational Techniques and Calculations) to gain an understanding of the conceptual workings of ERGM. This site can then provide detail on how to use the software.

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Simulation

Agent-based simulation has become an important tool in network research. Simulation studies permit researchers to model complex interactions between individual nodes connected in a network. Macy and Willer 2002 introduces the rationale for simulation using agent-based models (AGMs) and distinguishes this method from other social-scientific approaches. Findings in this line of research confirm two basic hypotheses: many social phenomena of interest, in particular desirable and pathological outcomes of collective properties, can be reproduced by prescribing the networks through which individuals may communicate; and successful collective actions (Marwell, et al. 1988), strong conformity to absurd views (Centola, et al. 2005), and explosions of misinformation (Ma, et al. 2010) can all be reproduced, or suppressed, by modifying the structure of a communication network. In studies in which network structure is a dependent variable, Carley 1991 shows that isolated, frozen components of like-minded individuals can be produced from a simple set of communication rules. Contractor and Grant 1996 shows that the emergence of such components can be accelerated or avoided with different initial network configurations. In a challenge to the field, Centola and Macy 2007 demonstrates the importance of a model’s assumptions about the nature of communication processes.

  • Carley, Kathleen. 1991. A theory of group stability. American Sociological Review 56.3: 331–354.

    DOI: 10.2307/2096108Save Citation »Export Citation »E-mail Citation »

    The rationale of this model is at the core of many network studies. Carley created a simple iterative model in which the propensity for individuals to share knowledge with one another is a probabilistic function of the knowledge they already share in common. The result is that communities converge into homogeneous clusters in which everyone shares knowledge only with those who already agree with them. Available online for purchase.

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  • Centola, Damon, and Michael Macy. 2007. Complex contagions and the weakness of long ties. American Journal of Sociology 113.3: 702–734.

    DOI: 10.1086/521848Save Citation »Export Citation »E-mail Citation »

    In this model of diffusion, the authors operationalize the idea of “complex contagions” as contagions in which transmission of a message or idea requires exposure from at least two sources. Results of this modest modification to traditional diffusion models suggest that the dynamics of small-world networks and other established structures are substantially different for complex contagions. Available online for purchase.

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  • Centola, Damon, Robb Willer, and Michael Macy. 2005. The emperor’s dilemma: A computational model of self-enforcing norms. American Journal of Sociology 110.4: 1009–1040.

    DOI: 10.1086/427321Save Citation »Export Citation »E-mail Citation »

    This simulation suggests that network position can matter more than raw numbers in the diffusion of a belief. Centola and colleagues simulate the adoption of a belief as the result of the proportion of an individual’s network neighbors that currently hold it. The authors find that when “true believers” are closely connected to one another, they become a powerful, persuasive block, even if there are few of them in total. Available online for purchase.

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  • Contractor, Noshir S., and Susan J. Grant. 1996. The emergence of shared interpretations in organizations: A self-organizing systems perspective. In Dynamic patterns in communication processes. Edited by James H. Watt and C. Arthur VanLear, 215–230. Thousand Oaks, CA: SAGE.

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    Similar to Carley 1991, the authors model communication as both a cause and a consequence of shared ideas. In this model, however, ideas are modeled as interpretations that are sensitive to random disturbance. Results show that heterogeneity in beliefs leads to less communication at first, but that in the long run there is a greater degree of communication across a large number of individuals.

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  • Lazer, David, and Allan Friedman. 2007. The network structure of exploration and exploitation. Administrative Science Quarterly 52.4: 667–694.

    DOI: 10.2189/asqu.52.4.667Save Citation »Export Citation »E-mail Citation »

    This study uses many novel simulation techniques to demonstrate March’s classic trade-off between exploration and exploitation. Results indicate that, consistent with March’s theory of exploration and exploitation, complex problems are better solved by more loosely connected networks. Available online for purchase.

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  • Ma, X. J., W. X. Wang, Y. C. Lai, and Z. Zheng. 2010. Information explosion on complex networks and control. European Physical Journal B: Condensed Matter and Complex Systems 76.1: 179–183.

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    Most studies assume that what is sent over network links is unmodified. These authors examine the impact of “distortion”—a mutation or error in a communication’s message. They find that “information explosion,” the proliferation of distorted forms through the community’s memory, is more probable when individuals favor communication to the most central nodes. Available online for purchase.

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  • Macy, Michael W., and Robert Willer. 2002. From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology 28:143–166.

    DOI: 10.1146/annurev.soc.28.110601.141117Save Citation »Export Citation »E-mail Citation »

    Provides the basic rationale for ABMs as tools of theory building and exploration. In ABMs a small set of simple behavioral rules of action and learning is distributed among a set of individuals that then interact with one another to produce a set of recognizable collective outcomes, such as social clustering or collective action. The authors review the major approaches and findings in ABM research. Available online by subscription.

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  • Marwell, Gerald, Pamela E. Oliver, and Ralph Prahl. 1988. Social networks and collective action: A theory of critical mass. III. American Journal of Sociology 94.3: 502–534.

    DOI: 10.1086/229028Save Citation »Export Citation »E-mail Citation »

    Simulates collective action and free-riding problems, with network structure as the independent variable. The authors cleverly capture interaction effects between network variables and other group properties, such as resource and interest homogeneity, via the analysis of simulation results in ordinary least squares (OLS) regression. Available online for purchase.

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Diffusion

One of the first areas of inquiry to rely heavily on network techniques is research on the diffusion of innovations. Networks play a central role in Rogers 2003, the classic text on diffusion. Coleman, et al. 1957 shows that “networks matter” in the prescription of new medication, a finding that ran counter to the prevailing assumption that doctors made medical determinations using independent judgments of medical facts. Burt 1987 later provides an alternative explanation for how networks matter, dividing explanations of network influence into “cohesion” arguments and “structural equivalence” arguments. The cohesion argument suggests that individuals adopt innovations because network ties serve as conduits for information and communication. The structural equivalence argument suggests individuals adopt innovations because network ties determine a set of social expectations. For the most part, scholars have continued with the “cohesion” rationale suggested by Coleman, et al. 1957. Valente 1995 offers a complement to Rogers’s work with a variety of formal model specifications with a particular emphasis on “network exposure,” the fraction of an individual’s communication partners that have already adopted the innovation. Friedkin and Johnsen 1990 provides a formal model designed specifically for the diffusion of beliefs and opinion. Watts and Strogatz 1998 begins a new emphasis on examining the network structure for its diffusion enhancing or impeding properties.

  • Burt, Ronald S. 1987. Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology 92.6: 1287–1335.

    DOI: 10.1086/228667Save Citation »Export Citation »E-mail Citation »

    Burt clearly and thoroughly articulates the debate between cohesion explanations and structural equivalence explanations of contagion and influence. Burt finds support for the latter via a clever examination of the data in Coleman, et al. 1957. Available online for purchase.

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  • Coleman, James, Elihu Katz, and Herbert Menzel. 1957. The diffusion of an innovation among physicians. Sociometry 20.4: 253–270.

    DOI: 10.2307/2785979Save Citation »Export Citation »E-mail Citation »

    One of the earliest and best-known studies showing the importance of social influence. The authors provide evidence that social networks among physicians influence their likelihood of prescribing a new medication. Available online for purchase.

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  • Friedkin, Noah E., and Eugene C. Johnsen. 1990. Social influence and opinions. Journal of Mathematical Sociology 15.3–4: 193–205.

    DOI: 10.1080/0022250X.1990.9990069Save Citation »Export Citation »E-mail Citation »

    Provides a formal model under which various theories of diffusion can be subsumed. The key parameters include the weight of interpersonal influence in relation to the influence of exogenous factors. The authors show that both opinion divergence and convergence can be accommodated by the general model proposed. This is a good place to start conceptually before building a complex computational model of diffusion. Available online for purchase.

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  • Rogers, Everett M. 2003. Diffusion of innovations. 5th ed. New York: Free.

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    Rogers’s classic work on diffusion. The text provides explanation and definition of the main concepts in the field (e.g., the S-curve, the notion of adoption) as well as a host of citations to empirical studies in the area. This is in the small canon of works that all network researchers are presumed to know.

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  • Valente, Thomas W. 1995. Network models of the diffusion of innovations. Quantitative Methods in Communication. Cresskill, NJ: Hampton.

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    Emphasizes the operationalization of formal models of diffusion, with examples from prior research. Particular attention is paid to threshold and critical mass models. For a researcher who has a theoretical idea for a study of diffusion but is unsure of implementation, this text is an excellent reference.

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  • Watts, Duncan J., and Steven H. Strogatz. 1998. Collective dynamics of “small-world” networks. Nature 393.6684: 440–442.

    DOI: 10.1038/30918Save Citation »Export Citation »E-mail Citation »

    Network researchers regularly refer to small-world networks, with minimal explanation. This article introduces the idea of small-world networks as those that contain substantial clustering, such as lattices, as well as short path lengths, such as random networks. The authors find small-world properties in several observed networks and show that ease of diffusion is an important implication of small-world graphs. Available online for purchase.

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Evolution

Since the late 20th century, network scholars have taken an increasing interest in the way networks grow and change. Barabási and Albert 1999 finds that many distinct networks share a common structure, and the article provokes the need for explanation. Scholars generally agree that larger network structures, such as clusters, emerge from an aggregation of individual, nodal decisions, but there is little consensus on the way in which these larger structures, in turn, influence these decisions. Burt 1995 suggests that optimal logics are invariant with network structure or node position—nodes always play the same strategy—whereas Powell, et al. 2005 contends that the desirable strategy shifts with position. Barnett 2001 finds that, despite substantial local change, core network structure is stable over time. Monge, et al. 2008 argues that networks may be unstable, given a set of logics and a particular structure, a position supported by evidence provided by Palla, et al. 2007.

  • Barabási, Albert-László, and Réka Albert. 1999. Emergence of scaling in random networks. Science 286.5439: 509–512.

    DOI: 10.1126/science.286.5439.509Save Citation »Export Citation »E-mail Citation »

    This work is one of the first to propose that network structures might be the result of simple, micro-, local-level tendencies accumulated over a large scale. Authors propose and find evidence that “preferential attachment,” in which new nodes entering a network are more likely to link to those nodes that already have the most connections, may explain the structure. Available online by subscription.

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  • Barnett, George A. 2001. A longitudinal analysis of the international telecommunication network, 1978–1996. American Behavioral Scientist 44.10: 1638–1655.

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    Examines longitudinal change in the overall structure of a large-scale network over a substantial period of time. This analytical approach permits the author to compare structural change to changes in macrolevel indicators of economic strength and development. Available online for purchase.

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  • Burt, Ronald S. 1995. Structural holes: The social structure of competition. Cambridge, MA: Harvard Univ. Press.

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    Burt contributes to the theories of network change by proposing a structural variable— the structural hole and its complement, the bridge—that serves as both a cause and a consequence of social action. In particular, Burt suggests that because structural holes are a source of competitive advantage, they will be identified and exploited over time, meaning, they will be “filled in,” thereby reducing the opportunity for further network change by this strategy.

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  • Monge, Peter, Bettina M. Heiss, and Drew B. Margolin. 2008. Communication network evolution in organizational communities. Communication Theory 18.4: 449–477.

    DOI: 10.1111/j.1468-2885.2008.00330.xSave Citation »Export Citation »E-mail Citation »

    Describes network change in terms of evolutionary theory. The authors use this evolutionary perspective to suggest that networks among and within organizations are likely to follow patterns of growth, differentiation, and decline similar to those found in biological communities. The notion of relational carrying capacity is introduced as the limit to the number of communication links that can be sustained in a network. Available online for purchase.

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  • Palla, Gergely, Albert-László Barabási, and Tamás Vicsek. 2007. Quantifying social group evolution. Nature 446.7136: 664–667.

    DOI: 10.1038/nature05670Save Citation »Export Citation »E-mail Citation »

    An important work examining the dynamics of change in large networks. The authors find that the best strategies for sustaining communities of communicating individuals are dependent on the size of the community. Results indicate that small communities survive longer when there is minimal turnover in membership: few nodes enter or leave the community. In contrast, large communities survive longer when there is a higher level of turnover. Available online for purchase.

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  • Powell, Walter W., Douglas R. White, Kenneth W. Koput, and Jason Owen-Smith. 2005. Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology 110.4: 1132–1205.

    DOI: 10.1086/421508Save Citation »Export Citation »E-mail Citation »

    The authors shift the emphasis of interorganizational network analysis from the dyad to the field level. The authors test four competing logics of attachment. Results show that favoring one logic over another depends on the position of an organization as well as the overall network structure. The electronic version of the article also includes “movies” for visualizing network change. Available online for purchase.

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Health

When it comes to health communication and related research, networks seem to crop up in strange places. The curiosity stems from two presumptions that have been repeatedly challenged but that have nonetheless survived as the basis of much theory: individuals make health decisions based solely on objective, scientific information and evidence, and individuals’ very conceptions of scientific information and evidence are free of social influence. Coleman, et al. 1957 provides evidence of social influence where it “shouldn’t be”—in the minds of doctors prescribing medication. Christakis and Fowler 2007 and Christakis and Fowler 2008 provide evidence of social influence in individual health behaviors. Their work on obesity (Christakis and Fowler 2007) and smoking (Christakis and Fowler 2008) provides further evidence that social norms maintained within local clusters of a social network influence health. Bearman, et al. 2004 shows that network structures are also formed by normative logics, impacting the diffusion of disease and intervention treatments. Pescosolido 2006 presents these conceptual issues in thorough detail in a conceptual review of the field and its future. Valente 2010 offers a comprehensive, state-of-the art guide for conducting research in this area.

  • Bearman, Peter S., James Moody, and Katherine Stovel. 2004. Chains of affection: The structure of adolescent romantic and sexual networks. American Journal of Sociology 110.1: 44–91.

    DOI: 10.1086/386272Save Citation »Export Citation »E-mail Citation »

    The authors provide evidence that networks of adolescent sexual partnerships show a “spanning tree” structure rather than a strong core structure, as often assumed in diffusion research. Spanning tree networks show longer paths between nodes and are reliant on more nodes, including many noncentral nodes, to maintain connectivity. When transmission networks are organized as spanning trees, classic intervention strategies that focus on central nodes may be less effective.

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  • Christakis, Nicholas A., and James H. Fowler. 2007. The spread of obesity in a large social network over 32 years. New England Journal of Medicine 357.4: 370–379.

    DOI: 10.1056/NEJMsa066082Save Citation »Export Citation »E-mail Citation »

    The authors provide evidence that the condition of obesity, largely considered to be based on genetic and psychological factors unrelated to social circumstance, is socially contagious. The study is considered important for supporting the hypothesis that social networks have a far-reaching and largely undiscovered impact on behavior.

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  • Christakis, Nicholas A., and James H. Fowler. 2008. The collective dynamics of smoking in a large social network. New England Journal of Medicine 358.21: 2249–2258.

    DOI: 10.1056/NEJMsa0706154Save Citation »Export Citation »E-mail Citation »

    The authors report results from a longitudinal study tracking smokers and their social networks. Results indicate that the decline in smoking across the entire population occurred through changes at the level of social clusters, rather than individuals. Individual smokers also tended to be on the periphery of the overall network.

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  • Coleman, James, Elihu Katz, and Herbert Menzel. 1957. The diffusion of an innovation among physicians. Sociometry 20.4: 253–270.

    DOI: 10.2307/2785979Save Citation »Export Citation »E-mail Citation »

    The authors provide evidence that social networks among physicians influence their likelihood of prescribing a new medication, suggesting that their decisions are based on factors other than personal observations or findings reported in scientific journals. Available online for purchase.

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  • Pescosolido, Bernice A. 2006. Of pride and prejudice: The role of sociology and social networks in integrating the health sciences. Journal of Health and Social Behavior 47.3: 189–208.

    DOI: 10.1177/002214650604700301Save Citation »Export Citation »E-mail Citation »

    Pescosolido argues that future health science research will rely on an integrated understanding of biological and social factors and suggests that an emphasis on understanding social networks can integrate the two. Useful for researchers who have an interest in the overlap between health care outcomes and networks, as the author articulates the potential for mutual contributions of these fields in both conceptual depth and concrete detail. Available online for purchase.

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  • Valente, Thomas W. 2010. Social networks and health: Models, methods, and applications. Oxford and New York: Oxford Univ. Press.

    DOI: 10.1093/acprof:oso/9780195301014.001.0001Save Citation »Export Citation »E-mail Citation »

    Provides a concise and accessible review of early-21st-century network research, with an emphasis on network-based explanations for individual behaviors. Of particular interest to health behavior researchers is the book’s extensive treatment of “network interventions”—health campaigns introduced to strategically positioned individuals—and methods for tracking the diffusion of the effects of such campaigns.

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Exchange Networks and Rational Models

A modest but growing body of research examines the way that network structures both constrain and are sometimes manipulated by individuals behaving according to “rational principles” of self-interest and arm’s-length exchange. These models emphasize the manner in which networks open or limit avenues to information or alternative opportunities for exchange and the possibility for creating, modifying, or undermining stable equilibria through the manipulation of network structure. Burt 1995 provides a thorough theoretical articulation of linking and position in terms of investments and benefits. Jackson and Wolinsky 1996 is the first article to describe the maintenance of links with a formal, rational model. Bearman 1997 extends an understanding of rational calculation to include unarticulated norms of exchange in a nonclassical setting. Easley and Kleinberg 2010 offers a review of the more recent major findings in this area. In a critique of these approaches, Granovetter 1985 argues that attendance to network influences undermines the assumptions of rational models.

  • Bearman, Peter. 1997. Generalized exchange. American Journal of Sociology 102.5: 1383–1415.

    DOI: 10.1086/231087Save Citation »Export Citation »E-mail Citation »

    This insightful article combines a deep explanation of the social nature and implications of the act of “exchange” with innovative methodology. The concept of generalized exchange, in which goods move in one direction through a cycle of at least three individuals, is introduced. One of the only articles in network research to focus on cycles: closed triads or larger structures in which no links are directly reciprocated.

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  • Burt, Ronald S. 1995. Structural holes: The social structure of competition. Cambridge, MA: Harvard Univ. Press.

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    Burt elaborates the advantages of occupying structural holes, such as access to unique information and the ability to extract rents through the restriction of access to the other side of the hole. This work calls attention to the idea of networks as active, strategic creations of individuals rather than simply as given environments that individuals accept.

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  • Easley, David, and Jon Kleinberg. 2010. Networks, crowds, and markets: Reasoning about a highly connected world. New York: Cambridge Univ. Press.

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    Although not exclusively about exchange networks, this text emphasizes network dynamics from a rational, strategic point of view. Several studies that deploy networks in bidding and exchange games are reviewed.

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  • Granovetter, Mark. 1985. Economic action and social structure: The problem of embeddedness. American Journal of Sociology 91.3: 481–510.

    DOI: 10.1086/228311Save Citation »Export Citation »E-mail Citation »

    Granovetter directly critiques the assumptions of neoclassical economic and related rationalist approaches to human behavior from the point of view of networks. The argument can be summarized by the claim “position matters,” but this simple upshot does not do the paper justice. A must read for anyone considering the relationship between networks and economics, markets, or other forms of exchange.

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  • Jackson, Matthew O., and Asher Wolinsky. 1996. A strategic model of social and economic networks. Journal of Economic Theory 71.1: 44–74.

    DOI: 10.1006/jeth.1996.0108Save Citation »Export Citation »E-mail Citation »

    A formal treatment of networks as a means of distributing labor and utility by rational, self-interested individuals. The article is densely packed with formal analytics, including proofs. However, the illustrative models of worker–firm negotiations and scholarly coauthorship are useful for conceptualizing labor allocation in network terms. The authors also demonstrate a fundamental trade-off between network efficiency and network stability. Available online for purchase.

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Social Capital

Social capital research focuses on the benefits that individuals or collectivities can obtain from the structure of their social networks. Social capital research emphasizes two resources provided by social relationships: access to diverse information and enforcement of rules and standards that coordinate action and create a basis for trust. Researchers in this area tend to discuss the benefits to individuals and to the communities in which they interact together on the understanding that social capital exists only where it can be replenished and sustained by repeated action. Coleman 1988 provides a definition of social capital and its basic theoretical rationale. Not surprisingly, an important (and occasionally overlooked) distinction in this area is the difference between social capital as an individual asset and social capital as a community property. Organizational approaches emphasize the former, whereas collective action approaches emphasize the latter. New research in online communities examines both and suggests ways in which these properties influence one another.

  • Coleman, James S. 1988. Social capital in the creation of human capital. In Special issue: Supplement: Organizations and institutions: Sociological and economic approaches to the analysis of social structure. Edited by Christopher Winship and Sherwin Rosen. American Journal of Sociology 94:S95–S120.

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    The classic article on the subject even though the term social capital is used loosely and with many context-specific variants in much of the literature. This article is a good starting point for those seeking to grasp the fundamental idea in the concept. Coleman specifies the meaning of social capital and articulates abstract, general theoretical arguments in support of the concept and its uses. Available online for purchase.

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Organizations

Research on social capital in the context of organizations emphasizes the benefits, or “returns,” that individuals or individual organizations can gain by investing their resources in building personal network configurations in a particular way. Uzzi 1997 reports on the benefits, and risks, to individual organizations for holding a mix of arm’s-length transaction ties and more intimate, socially embedded ties. Mehra, et al. 2001 uncovers the personal traits of those that obtain advantageous network positions. Burt 2005 shows how individuals in brokerage positions are able to capitalize on innovations produced by others in more structurally embedded locales. This finding suggests a tension between what is best for the individual and what is best for the network.

  • Burt, Ronald S. 2005. Brokerage and closure: An introduction to social capital. Clarendon Lectures in Management Studies. Oxford and New York: Oxford Univ. Press.

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    Burt summarizes the arguments and findings regarding the benefits of the two competing forms of social capital. Burt’s arguments for brokerage are well known and largely repeated from his prior work, but these arguments are well integrated with empirical support for the benefits of embeddedness.

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  • Mehra, Ajay, Martin Kilduff, and Daniel J. Brass. 2001. The social networks of high and low self-monitors: Implications for workplace performance. Administrative Science Quarterly 46.1: 121–146.

    DOI: 10.2307/2667127Save Citation »Export Citation »E-mail Citation »

    The authors examine the relationships among the degree to which individuals “self-monitor” their behavior for social appropriateness, their position in a workplace social network, and their workplace performance. Results indicate that high self-monitors obtain more central positions in the network and that these central positions independently contribute to improved performance. Available online for purchase.

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  • Uzzi, Brian. 1997. Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly 42.1: 35–67.

    DOI: 10.2307/2393808Save Citation »Export Citation »E-mail Citation »

    Uzzi reports findings from his fieldwork in the New York City fashion industry. The results suggest that, in contrast to classic models of the firm and rational behavior, organizations operate by very different rules when working with embedded ties rather than arm’s-length ties. Uzzi reports both the benefits and the drawbacks of embedded ties. Available online for purchase.

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Online Communities

Research on social capital in online communities examines the impact of collective networks on individual well-being and the impact of individual behaviors on community development. Wellman, et al. 2001 shows that participation in online communities modifies individual participation in civic pursuits. Ellison, et al. 2007 finds that Facebook facilitates social bridging while improving well-being for certain kinds of individuals. Donath 2007 presents a research agenda for examining the ability of social networking sites to foster lasting community. Huffaker 2010 finds evidence that the power of these sites to do so may reside in the degree to which certain individuals step forward and take leadership roles that encourage community development.

  • Donath, Judith. 2007. Signals in social supernets. Journal of Computer-Mediated Communication 13.1: 231–251.

    DOI: 10.1111/j.1083-6101.2007.00394.xSave Citation »Export Citation »E-mail Citation »

    Introduces signaling theory for evaluating the potential of social network sites to build large, sustained social networks, or “supernets.” The author argues that discerning reliable, honest representations (signals) of unobservable personal traits and states of mind is essential to communication on these sites. This article provides an excellent articulation of signaling theory and a novel and important agenda for research into online communities and social networking.

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  • Ellison, Nicole B., Charles Steinfeld, and Cliffe Lampe. 2007. The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication 12.4: 1143–1168.

    DOI: 10.1111/j.1083-6101.2007.00367.xSave Citation »Export Citation »E-mail Citation »

    A seminal study applying social capital theory to participation in an online community. Authors examine the degree to which participation in Facebook leads individuals to perceive they have increased social capital. Results suggest that Facebook can help college students build both bridging and bonding social capital but that it is more useful for building the former.

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  • Huffaker, David. 2010. Dimensions of leadership and social influence in online communities. Human Communication Research 36.4: 593–617.

    DOI: 10.1111/j.1468-2958.2010.01390.xSave Citation »Export Citation »E-mail Citation »

    Noteworthy for its integration of social network and linguistic measures, this article integrates insights from persuasion theory and message design into an analysis of Google group posts. The study finds support for a variety of hypotheses regarding the posting habits, network position, and linguistic choices made by those who are best able to spark conversation in an online community. Available online for purchase.

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  • Wellman, Barry, Anabel Quan Haase, James Witte, and Keith Hampton. 2001. Does the Internet increase, decrease, or supplement social capital? Social networks, participation, and community commitment. American Behavioral Scientist 45.3: 436–455.

    DOI: 10.1177/00027640121957286Save Citation »Export Citation »E-mail Citation »

    For years there has been debate over whether Internet use supplements or undermines individuals’ abilities to build lasting, supportive relationships and community. This article focuses the debate with a clear, succinct review of the major arguments and findings supporting both positions. Analysis of the National Geographic survey reveals Internet usage patterns and their relationship to social and political participation. Available online for purchase.

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Collective Action

Scholars interested in collective action problems have examined the influence of network structures to foster or suppress successful collective action. Marwell, et al. 1988 models free riding under different network structures. Diani and McAdam 2003 provides several examples in which network structure influences civic and social movements. Shumate and Lipp 2008 argues that networks are “communal public goods.”

Organizational Networks

For the most part, research into organizational networks has defined questions in terms of relationships between organizations (interorganizational networks) or relationships between individuals working in organizational contexts, such as work teams, small groups, or collaborations.

Interorganizational Networks

Research in interorganizational networks has grown substantially. Two early works examine the motivations and processes behind interorganizational networking. Gulati 1995 initiates empirical examination of a long-standing question in organizational and economic theory: why do organizations form alliances? Eisenberg, et al. 1985 theorizes how and why such networks are achieved and maintained through communicative processes. As the number and relevance of interorganizational partnerships have grown in a variety of fields, research has uncovered interesting tendencies. Uzzi 1997 suggests that organizations benefited from an appropriate mixture of embedded and arm’s-length ties. Gulati and Higgins 2003 finds that appropriateness is contingent on environmental uncertainty. Ingram and Roberts 2000 determines that these benefits can be achieved through interpersonal relationships between business managers, whereas Reagans and McEvily 2003 advances that these benefits are dependent on the qualities of the knowledge shared and needed between organizations. Powell, et al. 2005 finds evidence that organizational linking logics are influenced by their network position, a thesis that has important implications for the study of network change over time. Provan, et al. 2007 introduces questions related to the governance of these evolving networks.

  • Eisenberg, E., Richard V. Farace, Peter R. Monge, et al. 1985. Communication linkages in interorganizational systems: Review and synthesis. In Progress in communication sciences. Vol. 6. Edited by Brenda Dervin and Melvin J. Voight, 231–261. Norwood, NJ: Ablex.

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    Interorganizational network research generally focuses on long- or medium-term relationships between large or complex institutions. This article provides a helpful conceptual framework and typology for parsing interorganizational linkages into manageable constructs. Particular attention is paid to the role of the individual as the conduit of interorganizational activity. The authors point out that some interorganizational activity is interpersonal but that other activities are institutional and merely carried by individuals.

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  • Gulati, Ranjay. 1995. Social structure and alliance formation patterns: A longitudinal analysis. Administrative Science Quarterly 40.4: 619–652.

    DOI: 10.2307/2393756Save Citation »Export Citation »E-mail Citation »

    Gulati argues that because social structure is both a determinant and an outcome of interorganizational linking, the dynamics of interorganizational ties can only be regulated, not controlled. Gulati distinguishes between relational influences (direct influences by partners on one another) and structural influences (the influence of nodes in nearby alliances). The author finds support for hypotheses suggesting there is a limit to the duration and intensity of alliances. Available online for purchase.

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  • Gulati, Ranjay, and Monica C. Higgins. 2003. Which ties matter when? The contingent effects of interorganizational partnerships on IPO success. Strategic Management Journal 24.2: 127–144.

    DOI: 10.1002/smj.287Save Citation »Export Citation »E-mail Citation »

    Explores the benefits and costs of interorganizational network embeddedness under different environmental conditions. Rather than treating the number of ties as appropriate or inappropriate based on some universal standard, the authors consider the benefits of establishing ties to particular kinds of organizations relative to the degree of uncertainty of the environment and the attention available for evaluating this uncertainty. Available online for purchase.

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  • Ingram, Paul, and Peter W. Roberts. 2000. Friendships among competitors in the Sydney hotel industry. American Journal of Sociology 106.2: 387–423.

    DOI: 10.1086/316965Save Citation »Export Citation »E-mail Citation »

    Illustrates how interpersonal network ties are relevant in the interorganizational context. Friendships among hotel managers both predict and are predicted by the success of the hotels. Available online for purchase.

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  • Powell, Walter W., Douglas R. White, Kenneth W. Koput, and Jason Owen-Smith. 2005. Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology 110.4: 1132–1205.

    DOI: 10.1086/421508Save Citation »Export Citation »E-mail Citation »

    Shifts the emphasis of interorganizational network analysis from the dyad to the field level using longitudinal network data in an investigation of the biotechnology field. The authors test four competing logics of attachment: accumulative advantage, similarity, follow the trend, and multiconnectivity. Results show that favoring one logic over another depends on the position of an organization as well as the overall network structure.

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  • Provan, Keith G., Amy Fish, and Joerg Sydow. 2007. Interorganizational networks at the network level: A review of the empirical literature on whole networks. Journal of Management 33.3: 479–516.

    DOI: 10.1177/0149206307302554Save Citation »Export Citation »E-mail Citation »

    Reviews and synthesizes research from twenty-six studies of interorganizational networks and reports general patterns in the development of “whole networks” of organizations (as opposed to patterns in dyadic relations or individual organizational linking patterns). The authors propose a variety of arguments relating this finding to the problem of network governance: the setting of rules for how business may be conducted in the network. Available online for purchase.

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  • Reagans, Ray, and Bill McEvily. 2003. Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly 48.2: 240–267.

    DOI: 10.2307/3556658Save Citation »Export Citation »E-mail Citation »

    This study is one of the few to examine directly and compare the benefits of both spanning ties that provide knowledge variety and cohesive ties that encourage trust and knowledge sharing. The authors find support for the benefits of both cohesion and range. Network benefits appear to be strongest when knowledge is tacit and when substantial baseline knowledge is already shared between firms. Available online for purchase.

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  • Uzzi, Brian. 1997. Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly 42.1: 35–67.

    DOI: 10.2307/2393808Save Citation »Export Citation »E-mail Citation »

    A report from fieldwork in the New York City fashion industry. In contrast to classic models of the firm and rational behavior, the results suggest that organizations operate by very different rules when working with embedded ties (in contrast to arm’s-length ties). The author reports benefits and drawbacks of embedded ties. Available online for purchase.

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Teams, Small Groups, and Collaboration

There are two basic approaches to network research in small groups and teams (see Katz, et al. 2004): researchers examine the internal network structure of groups or teams, or they examine the social network structure in the environment team members are drawn from. Cummings and Cross 2003 examines the impact of communication network structure on group performance. Guimera, et al. 2005 looks at how the choosing of teammates from the larger network impacts group performance. Moody 2004 studies the impact of group formation on the structure of the network over time.

  • Cummings, Jonathon N., and Rob Cross. 2003. Structural properties of work groups and their consequences for performance. Social Networks 25.3: 197–210.

    DOI: 10.1016/S0378-8733(02)00049-7Save Citation »Export Citation »E-mail Citation »

    One of the few empirical studies of networks within natural, small work groups. Examines 182 work groups in a large global organization. The study emphasizes network structures that constrain communication, such as hierarchy and centralization, and finds that these constraints inhibited performance on new and complex projects. Available online for purchase.

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  • Guimerà, Roger, Brian Uzzi, Jarrett Spiro, and Luís A. Nunes Amaral. 2005. Team assembly mechanisms determine collaboration network structure and team performance. Science 308.5722: 697–702.

    DOI: 10.1126/science.1106340Save Citation »Export Citation »E-mail Citation »

    This is a springboard to the study of team formation and collaboration on a large scale. Homophily is believed to improve communication and cooperation, whereas diversity is believed to improve creativity. The authors test the effects of these countervailing tendencies in a major longitudinal study of teams formed in both science and the arts.

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  • Katz, Nancy, David Lazer, Holly Arrow, and Noshir Contractor. 2004. Network theory and small groups. Small Group Research 35.3: 307–332.

    DOI: 10.1177/1046496404264941Save Citation »Export Citation »E-mail Citation »

    A comprehensive explanation and review of network research in the area of small groups. Includes a theoretical and methodological primer on network concepts important for group researchers as well as a historical review. Available online for purchase.

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  • Moody, James. 2004. The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review 69.2: 213–238.

    DOI: 10.1177/000312240406900204Save Citation »Export Citation »E-mail Citation »

    Moody examines collaboration from a field perspective. What does the coauthorship network say about the dynamics of change within the field of sociology? Analysis of k-component connectivity (used to measure “structural cohesiveness”) shows that the field of sociology is well connected across subspecialties without relying on star authors as hubs.

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Networks of Communication Artifacts

Most network research identifies, analyzes, and theorizes relationships between human beings or their institutions. In the late 20th and early 21st centuries, however, researchers have begun to consider relationships between communication artifacts—the objects through which communication takes place—rather than just the individuals or institutions that engage in the communicative acts. Two areas that have existed for a while but that have gained new life with the advent of new communication technology are semantic networks and citation networks. Semantic networks analyze relationships between words. Citation networks analyze relationships between documents. Some scholars have proposed integrating the study of communicative artifacts into a new field called web science.

Semantic Networks

Networks connecting words, concepts, or meanings are generally referred to as semantic networks. Semantic networks are used to analyze text in a large number of research contexts, but there is little in the way of unified theory or established methodology. Three theoretical arguments, Callon, et al. 1983; Carley and Kaufer 1993; and Corman, et al. 2002, explain the “work” that words do within semantic networks. Yet, even with clear theory, Carley 1997 identifies an important methodological pitfall: the sensitivity of semantic network analysis to coding choices. In another line of research, large corpuses of text are analyzed for their structural properties (Ferrer i Cancho and Solé 2001). Steyvers and Tenenbaum 2005 offers a theoretical argument for the development of these structures. Van Atteveldt 2008 applies semantic network techniques to propose methods for the automated analysis of media content, such as frame extraction and sentiment analysis. Leskovec, et al. 2009 uses novel semantic network techniques for a longitudinal analysis of media content.

  • Callon, Michel, Jean-Pierre Courtial, William A. Turner, and Serge Bauin. 1983. From translations to problematic networks: An introduction to co-word analysis. Social Science Information/Sur les sciences sociales 22.2: 191–235.

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    Callon and colleagues identify words as stable objects that exert “force” through which activity is coordinated. The authors theorize that discourse is organized around “macro-terms” that, when traced, can reveal insight about the history and development of research on a topic. The article applies the theory to understanding change and development within scientific inquiry, but the arguments apply to other institutional settings as well. Available online for purchase.

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  • Carley, Kathleen M. 1997. Extracting team mental models through textual analysis. In Special issue: Computers can read as well as count. Journal of Organizational Behavior 18.S1: 533–558.

    DOI: 10.1002/(SICI)1099-1379(199711)18:1+%3C533::AID-JOB906%3E3.3.CO;2-VSave Citation »Export Citation »E-mail Citation »

    Presents the idea of mapping team mental models using semantic networks. An important contribution of the article is its demonstration of the sensitivity of this analysis to coding choices. Results also show that successful teams differ from unsuccessful teams only when data are unfiltered. Available online for purchase.

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  • Carley, Kathleen M., and David S. Kaufer. 1993. Semantic connectivity: An approach for analyzing symbols in semantic networks. Communication Theory 3.3: 183–213.

    DOI: 10.1111/j.1468-2885.1993.tb00070.xSave Citation »Export Citation »E-mail Citation »

    Semantic networks are a useful way to quantify text, but translating semantic network structures into meaningful theoretical or commonsense concepts is difficult. This article proposes a typology of “roles” played by words within discourse. Roles are based on the familiarity and structure of semantic connections emanating to and from the word. Roles have commonsense meaning, such as “ordinary words,” “buzzwords,” “allusions,” and “stereotypes.” Available online.

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  • Corman, Steven R., Timothy Kuhn, Robert D. McPhee, and Kevin J. Dooley. 2002. Studying complex discursive systems: Centering resonance analysis of communication. Human Communication Research 28.2: 157–206.

    DOI: 10.1093/hcr/28.2.157Save Citation »Export Citation »E-mail Citation »

    The authors introduce Centering Resonance Analysis (CRA) as a technique for textual analysis. Grounded in linguistic theory, CRA identifies and maps a network between words that participate in noun phrases within a text. Structural properties of the network can then be used to identify important words or to measure the overall coherence of a text. Available online for purchase.

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  • Ferrer i Cancho, Ramon, and Richard V. Solé. 2001. The small world of human language. Proceedings of the Royal Society B-Biological Sciences 268.1482: 2261–2265.

    DOI: 10.1098/rspb.2001.1800Save Citation »Export Citation »E-mail Citation »

    A network analysis of co-occurring words in the British National Corpus. The corpus demonstrates a core-periphery structure, suggesting that a language contains a core set of well-known terms that are well connected to one another and a peripheral set of poorly recognized terms with few connections. Available online by subscription.

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  • Leskovec, Jure, Lars Backstrom, and Jon Kleinberg. 2009. Meme-tracking and the dynamics of the news cycle. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 497–506.

    DOI: 10.1145/1557019.1557077Save Citation »Export Citation »E-mail Citation »

    Tracks the diffusion of a meme and its variants through newspapers and blogs. Memes are identified as components of connected text that share a common core of phrases. Results indicate that meme diffusion is influenced by a tendency to imitate what is common and to repeat what is recent. This combination creates life cycle curves of rapid growth and decline in meme usage. Available online for purchase.

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  • Steyvers, Mark, and Joshua B. Tenenbaum. 2005. The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science 29.1: 41–78.

    DOI: 10.1207/s15516709cog2901_3Save Citation »Export Citation »E-mail Citation »

    Provides a model of semantic growth that produces structural properties observed in real-world semantic networks. The model is based on the idea of differentiation, or specialization. New words are added to a corpus in connection with existing words for which they provide a narrower, more specialized meaning. These new words then take on some of the existing word’s connections.

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  • van Atteveldt, Wouter. 2008. Semantic network analysis: Techniques for extracting, representing, and querying media content. Charleston, SC: BookSurge.

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    Provides an authoritative overview and accessible description of available techniques for using semantic networks to code and analyze media content, particularly content related to politics and social issues.

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Web Science

Web science refers to research that treats the web as a phenomenon worthy of study in its own right. Berners-Lee, et al. 2006 and Hendler, et al. 2008 argue for the importance of web science as its own field and outline its primary goals and challenges. They identify three areas: the structure of the web, the semantic web, and the social and political implications of the web.

  • Berners-Lee, Tim, Wendy Hall, James Hendler, Nigel Shadbolt, and Daniel J. Weitzner. 2006. Creating a science of the web. Science 313.5788: 769–771.

    DOI: 10.1126/science.1126902Save Citation »Export Citation »E-mail Citation »

    Articulates the primary motivations for and challenges to development of web science. The authors suggest that web science encompasses the concerns of both basic and applied science, and they briefly discuss three major research challenges for this emerging field: the integration of mathematical models of web structure, the semantic web, and improved understanding of the web’s social consequences and appropriate approaches to governance.

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  • Hendler, James, Nigel Shadbolt, Wendy Hall, Tim Berners-Lee, and Daniel J. Weitzner. 2008. Web science: An interdisciplinary approach to understanding the web. Communications of the ACM 51:7: 60–69.

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    A more in-depth follow-up to Berners-Lee, et al. 2006. Argues for the study of the web as a phenomenon in its own right rather than as a particular instantiation of social and technological dynamics. From this perspective, the authors review and critique existing literature on the structure of the web. The importance of the semantic web and of understanding the social and political implications of the web’s evolution are also explained.

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Structure of the Web

The vast, linked structure of the web has been of substantial research interest in the natural, social, and computer sciences. Kleinberg, et al. 1999 provides a useful introduction to the topic. Barabási and Albert 1999 and Albert, et al. 1999 feature seminal discoveries that suggest that the web is a self-organizing phenomenon worthy of significant attention. Milo, et al. 2002 and Milo, et al. 2004 refine and support these findings.

  • Albert, Réka, Hawoong Jeong, and Albert-László Barabási. 1999. Internet: Diameter of the world-wide web. Nature 401.6749: 130–131.

    DOI: 10.1038/43601Save Citation »Export Citation »E-mail Citation »

    Another seminal article in web science. The authors analyze the web in terms of its average geodesic length and show that path lengths grow slowly as the network increases in size, suggesting that the web is a small-world network.

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  • Barabási, Albert-László, and Réka Albert. 1999. Emergence of scaling in random networks. Science 286.

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    A seminal article on web science in which the authors show that many large networks, including the web, have a scale-free structure characterized by a power-law distribution. As compared with random networks, scale-free networks have a greater number of nodes with an enormous number of connections. The presence of these distributions suggests that these networks form and grow according to principles that lead to self-organization. Available online by subscription.

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  • Kleinberg, Jon M., S. Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, and Andrew S. Tomkins. 1999. The web as a graph: Measurements, models, and methods. In Computing and combinatorics: 5th Annual International Conference, COCOON ’99, Tokyo, Japan, July 26–28, 1999. Edited by Takao Asano, Hiroshi Himai, D. T. Lee, Shin-ichi Nakano, and Takeshi Tokuyama, 1–17. Lecture Notes in Computer Science. Berlin and New York: Springer.

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    Introduces the concept of treating the Internet as a graph for the purposes of analysis. Although the findings are modest in comparison with subsequent studies, this article provides useful conceptual grounding. In particular, it articulates important assumptions of the research program.

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  • Milo, Ron, Shalev Itzkovitz, Nadav Kashtan, et al. 2004. Superfamilies of evolved and designed networks. Science 303.5663: 1538–1542.

    DOI: 10.1126/science.1089167Save Citation »Export Citation »E-mail Citation »

    The authors show that web motifs do appear to be shared by other social networks, which suggests that these networks are part of a common “superfamily” of networks. These results indicate that there is not likely to be a simple, universal rule of attachment by which networks evolve to achieve similar topology.

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  • Milo, R., S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. 2002. Network motifs: Simple building blocks of complex networks. Science 298.5594: 824–827.

    DOI: 10.1126/science.298.5594.824Save Citation »Export Citation »E-mail Citation »

    The authors find that the web shows an incidence of certain three- and four-node subgraph patterns, called motifs, that is greater than would be expected by random networks. However, motifs in the web are distinct from motifs in other networks that show scale structure, such as protein networks and neuronal networks.

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Semantic Web

The semantic web refers to a program of research and policy intending to make the documents and data on the web more machine searchable and readable. The end goal is to transform the web into an efficient, dynamic repository for the automated use and recombination of recorded human knowledge. Berners-Lee, et al. 2001 introduces the concept and its aims. Daconta, et al. 2003 provides a comprehensive explanation of key semantic web concepts and potential uses. The most controversial aspect of the semantic web is its reliance on stable “ontologies”: predefined rules of relationship and meaning between semantic concepts. Wilks 2008 gives a review of various critiques of ontologies characterized as logically or practically achievable. Mika 2007 offers evidence that ontologies can be improved with the addition of social network information. Van Atteveldt 2008 suggests ways in which the semantic web can be usefully integrated with large-scale text-mining techniques, using semantic networks. Shadbolt, et al. 2006 attempts to defend the semantic web against its critics and refine the research agenda in light of new developments.

  • Berners-Lee, Tim, James Hendler, and Ora Lassila. 2001. The semantic web. Scientific American, 17 May.

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    A short introduction to the basic ambitions and rationales for the semantic web. The article introduces the aims and basic logic of this research tool. Key ideas, such as resource description framework (RDF) triples, ontologies, and agents, are defined. Principles are illustrated with intuitive, commonsensical examples, and potential uses and benefits are explained.

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  • Daconta, Michael C., Leo J. Obrst, and Kevin T. Smith. 2003. The semantic web: A guide to the future of XML, web services, and knowledge management. Indianapolis, IN: Wiley.

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    Explores the potential of the semantic web for use by organizations and scholars of knowledge management; as such, it assumes readers may be unfamiliar with technical concepts and lingo. The text explains the semantic web and its constituent concepts (RDF, ontologies, uniform resource identifiers [URIs]) in clear detail, with realistic organizational examples.

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  • Mika, Peter. 2007. Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5.1: 5–15.

    DOI: 10.1016/j.websem.2006.11.002Save Citation »Export Citation »E-mail Citation »

    Adds a social dimension to the semantic web. Mika argues that meanings are stable only within social communities and proposes the inclusion of social networks in the extraction of ontologies from observed patterns of association between concepts. Results from two data sets indicate that associations between concepts derived from actor-concept networks improve the meaningfulness of the ontologies these associations comprise. Available online for purchase.

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  • Shadbolt, Nigel, Wendy Hall, and Tim Berners-Lee. 2006. The semantic web revisited. IEEE Intelligent Systems 21.3: 96–101.

    DOI: 10.1109/MIS.2006.62Save Citation »Export Citation »E-mail Citation »

    An update on the progress made and challenges ahead for the semantic web. The authors acknowledge and discuss the problem of appropriately defining and updating ontologies. Several concrete examples of applications in progress, particularly in the biomedical field, are also provided.

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  • van Atteveldt, Wouter. 2008. Semantic network analysis: Techniques for extracting, representing, and querying media content. Charleston, SC: BookSurge.

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    This text treats the semantic web as a research tool, providing insights into how semantic web data storage and management techniques might be used to improve automated analysis of media content.

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  • Wilks, Yorick. 2008. The semantic web as the apotheosis of annotation, but what are its semantics? IEEE Intelligent Systems 23.3: 41–48.

    DOI: 10.1109/MIS.2008.53Save Citation »Export Citation »E-mail Citation »

    A philosophical critique of the assumptions of semantic web research. Argues that the usefulness of the semantic web is predicated on an unrealistic reduction of knowledge from the multiplicities carried in natural language to a core set of stable, underlying meanings recorded in simpler codes (e.g., RDF triples). Provides arguments and examples from artificial intelligence, philosophy of language, and research on the semantic web. Available online for purchase.

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Implications of the Evolving Web

Many social scientists examine the sociopolitical impact of the web. Such research is constrained by the web, as it has been or is presently structured and used. Part of the program of web science is to anticipate the sociopolitical impact of the future of the web, based on the study of its natural evolution and in consideration of the influence of technologies currently under development. Despite these worthy goals, few studies have been executed in this ambitious line, yet important concerns have surfaced nonetheless; in particular, the enormous stores of previously private data now available in the shared “cloud” of information (Lazer, et al. 2009) and the increasing ease with which like-minded individuals can deploy search and other technologies to lock out challenging viewpoints (Adamic and Glance 2005). Turow and Tsui 2008 provides a set of critiques and analyses of these issues as they relate to hyperlinking on the web.

  • Adamic, Lada A., and Natalie Glance. 2005. The political blogosphere and the 2004 U.S. election: Divided they blog. In LinkKDD’05: Proceedings of the 3rd international workshop on link discovery. Edited by Jafar Adibi, 36–43. New York: ACM.

    DOI: 10.1145/1134271Save Citation »Export Citation »E-mail Citation »

    The impact of the web on political awareness and informed debate is an emerging research topic. This article, perhaps the most well known in the field, examines references between political blogs made through hyperlinks. Blogs of differing political views (liberal versus conservative) are unlikely to cite one another, suggesting that the freedom of choice offered by the blogosphere may serve to encourage political divisiveness.

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  • Lazer, David, Alex Pentland, Lada Adamic, et al. 2009. Computational social science. Science 323.5915: 721–723.

    DOI: 10.1126/science.1167742Save Citation »Export Citation »E-mail Citation »

    The authors enumerate several of the risks, particularly in regard to issues of privacy and data security, of researchers’ massive efforts to use data recorded on the web and other databases.

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  • Turow, Joseph, and Lokman Tsui, eds. 2008. The hyperlinked society: Questioning connections in the digital age. New Media World. Ann Arbor: Univ. of Michigan Press.

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    Examines the patterns and impact of hyperlinking on individuals, the media industry, and other social concerns.

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Co-Citation Networks

Citation analysis of scientific articles in a variety of disciplines has revealed the tendency toward a common, scale-free structure. Co-citation analysis uses the common citation of a given text by two or more citing texts to infer similarity between the citing texts. Most work in this area is exploratory and atheoretical. Price 1965 introduces the field. Small 1977 and Small 2006 provide analytic techniques for co-citation analysis. Börner, et al. 2010 offers suggestions for using Network Workbench for scientific networks. Evans 2008 uses citation analysis to make a counterintuitive, theoretical argument that the increased availability of research papers through the provision of online databases is leading scientists to make less use of prior research in the publications.

LAST MODIFIED: 01/11/2012

DOI: 10.1093/OBO/9780199756841-0025

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