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political science, international relations, psychology, public health, criminology, and even economics begin introducing ideas and methods of social network analysis into those disciplines. For example, Zeev Maoz (2012) analyzed international trade and military alliances as network processes. He found that international trade follows a preferential attachment or bandwagon process: all nations want a quick and short connection to a few key nations in the global trade network, resulting in a highly condensed, single-core structure. In contrast, for military alliances, nations tend to partner with countries sharing similar political ideologies and regime structures. This homophily preference produces a network configuration consisting of multiple small military alliance clusters that are only sparsely interconnected (see also Yang et al., 2016, p. 198).

      We would be remiss not to mention social media as an explosively growing component of social networks. Facebook, Twitter, LinkedIn, WeChat, and other apps facilitate a massive amount of daily information exchange among billions of users. Much social networking nowadays occurs in virtual spaces as users contact one another via computers, laptops, iPad tablets, and smartphones linked together by Ethernet cables or wireless. Computer communication networks and human social networks converge, engendering innumerable research opportunities and challenges for social and computer scientists. How does one best search, capture, aggregate, store, share, process, reduce, and visualize vast volumes of complex data generated by online social networkers (Press, 2013; Lohr, 2013)? John Mashey, chief scientist at Silicon Graphics, is often credited with coining the term Big Data, which he described in a slide presentation as “storage growing bigger faster” (1998, p. 2). Exponentially bourgeoning quantities of structured and unstructured information have revolutionized businesses, nonprofits, and governments. For social network researchers, Big Data is a trove of rich relational databases and a smörgåsbord of computer tools for data mining, information fusion, computational intelligence, machine learning, and other applications (de Nooy, Wouter, Mrvar, & Batagelj, 2018). Although Big Data enhances organizational operations and outcomes, it also raises numerous ethical and privacy challenges, such as the rise of surveillance state capacities to predict and control populations (Brayne, 2017; Madden, Gilman, Levy, & Marwick, 2017). Russian manipulation of the 2016 U.S. presidential election was only the most notorious of innumerable criminal abuses of Big Data on social media platforms. Calls for governmental regulation of social media companies encounter conundrums of how to protect platforms and safeguard free speech while prohibiting dangerous content (Berman, 2019). The fate of our democracy hangs in the balance.

      In sum, social network analysis is a vibrant multidisciplinary field. Peter Carrington and John Scott called it “a ‘paradigm’, rather than a theory or a method: that is, a way of conceptualizing and analyzing social life” (2011, p. 5). We believe the network paradigm has roots in and thrives on the integration of three elements: theories, methodologies, and applications. For theories, network analysis demands serious commitment that prioritizes actor interdependence and connectivity, emphasizing structured relations among social entities. For methodologies, network analysis borrows eclectically from diverse disciplines, collaborating across the aisles to create innovative procedures. For applications, people increasingly use their networking skills to navigate along complex interorganizational pathways to acquire desired goods and services, such as better healthcare, shopping bargains, and recreational experiences.

      This volume updates the second edition of Social Network Analysis by Knoke and Yang (2008). In addition to providing a general overview of fundamental methodological topics, we cover new developments of the past decade. Our approach is didactic, aimed primarily at graduate students and professionals in many social science disciplines, including sociology, political science, business management, anthropology, economics, psychology, public administration, public health, and human resources. College faculty could assign it as a text in graduate-level courses, use it for workshops at professional association meetings or summer instructional institutes, or study it to learn more about networks on their own. Graduate and advanced undergraduate students interested in social network analyses can read it to get a jump-start on their social network skills and intellectual aspirations. Professionals face many challenges in developing social network research, such as how to design a social network project, details and problems that may arise during network data collection, and alternative techniques for analyzing their social network data. Social network scholars may find this volume a useful brief refresher or reference book. For more advanced texts, we suggest Easley and Kleinberg (2010); Dorogovtsev and Mendes (2014); Lazega and Snijders (2015); de Nooy, Mrvar, and Batagelj (2018); and Newman (2010).

      We frequently illustrate concepts and methods by referring to substantive social network research problems, citing examples from children’s playgroups to organizations, communities, and international systems. We tried to write with a precision and freshness of presentation using concise language that minimizes technical complexities. The book consists of five substantive chapters. Chapter 2 introduces fundamental network assumptions and concepts, as applied to a variety of units of observation, levels of analysis, and types of measures. It contrasts relational contents and forms of relations and distinguishes between egocentric and whole networks. The structural approach emphasizes the value of network analysis for uncovering deeper patterns beneath the surface of empirical interactions. Chapter 3 concerns issues in collecting network data: boundary specification, data collection procedures, cognitive social structures, missing data, measurement error, and collecting online social media and Big Data. In Chapter 4, we discuss basic methods of network analysis, including graphs and matrices; centrality, prestige, and power; social distance, paths, walks, and reachability; transitivity and cliques; and size, centralization, density, and different measure of equivalence for pairs of actors or entities. Chapter 5 gives an overview of more-advanced methods of network analysis, including ego-nets; clustering, multidimensional analysis, and blockmodels; 2-mode and 3-mode networks; community detection; and exponential random graph models. The final section concludes with some speculations about future directions in social network analysis.

      After years of painstaking efforts, network analysts developed several computer packages to facilitate social network data collection and analyses. Softwares vary on many dimensions, such as operating systems, affordability, learning curves, and strengths and weaknesses. We attached an Appendix that summarizes some useful packages and contrasts them on those dimensions. We remain most impressed, however, with the breadth and user-friendly qualities of UCINET (Borgatti, Everett, & Freeman, 2002) as both a teaching and a research tool for smaller-scale social network analyses. Consequently, we used it to make this edition whenever we demonstrated social network analysis methods.

      Chapter 2 Network Fundamentals

      In this chapter, we discuss fundamental concepts for understanding social network analysis methods. We use terms and definitions most widespread and accepted by academic researchers but in instances of disagreement defer to sociological perspectives. We cite many examples from diverse disciplines that illustrate these basic concepts. Interested readers should read numerous publications to deepen their understanding of how network analysis methods can be applied to investigate substantive problems in their fields.

      To clarify the distinctive social network perspective on social action, a contrast to individualistic, variable-based approaches may be insightful. Many social science theories, possibly a large majority, assume that actors make decisions and act without regard to the behavior of other actors. Whether analyzed as utility-maximizing rational calculations or as drive-reduction motivation based on causal antecedents, such explanations primarily consider only the characteristics of persons while ignoring the broader interaction contexts within which social actors are embedded. In contrast, network analysis explicitly assumes that actors participate in social systems connecting them to other actors and that their relations comprise important influences on one another’s behaviors. Central to the theoretical and methodological agenda of network analysis is identifying, measuring, and testing hypotheses about the structural forms and substantive contents of relations among actors.

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