has a social network composed of faculty members, staff, students, and administrators. Multiple sets of relations suffuse such networks: collegial relations among faculty members, faculty advising graduate students, faculty instructing undergraduates, and administrators supervising faculty and staff. A police department is also structured as a formal social network, in which officers at the same rank are colleagues, whereas a quasimilitary chain of command establishes hierarchical authority relations. Typical order from top down would consist of chief of police, deputy chief, captain, lieutenant, sergeant, corporal, patrol officer.
Although people typically conceive the actors in social networks as human beings, they can just as well be collective entities or aggregated units, such as teams, groups, organizations, neighborhoods, political parties, and even nation-states. For example, corporations can engage in cooperative and competitive relations to pursue many outcomes, such as jointly developing new technologies and products or acquiring greater market shares (Knoke, 2001). Interorganizational relations take many governance forms, from contractual agreements to equity stakes (Child, 2005; Yang, Franziska, & Lu, 2016). Inside organizations, work groups and teams often engage in knowledge transfers or information sharing to facilitate innovation and improve task performance (Tsai, 2001). International relational networks also emerge and evolve, including military alliances and conflicts, trade partnerships and disputes, human migrations, intelligence exchanges, and technology sharing and embargoes (Yang et al., 2016, Chapter 8).
Nonsocial networks are prevalent in many domains: technology networks, computer networks and the Internet, telephone networks and electrical power grids, transportation and logistics networks, food delivery, and patent-citation networks. They share some similarities with social networks, except that instead of actors their units are physical entities, such as computers and transformers, and their relations are transmission and delivery lines such as Ethernet cables, wireless connections, airline routes, and interstate highways. We mention nonsocial networks primarily to note that networks are the subjects of studies by many disciplines besides the social sciences. Those investigations illuminate and inspire one another, engendering strong momentum to improve network knowledge, including social network analysis (Knoke & Yang, 2008). For example, after mathematicians developed graph theory, computer scientists applied it to construct optimal computer networks. Social network scholars can borrow algorithms from computer and mathematical sciences to decipher communication networks among friends, coworkers, and organizations.
Sociology built a long tradition of examining the social contexts of social networks. Founding fathers such as Georg Simmel, Émile Durkheim, and Max Weber promoted a structural perspective in the study of human behaviors. Social psychologist Jacob Moreno (1934) was directly responsible for laying the foundation of modern social network analysis. With Helen Jennings, Moreno invented sociometry to draw maps visualizing individuals and their interpersonal relations, revealing complex structural relations with simple diagrams. Moreover, Moreno and other pioneering social network scholars endeavored to explain how network structures affect human behaviors and psychological states (Freeman, 2004). On the one hand, we can better understand people’s actions and decisions by examining their social networks because networks provide participants with both opportunities and constraints. On the other hand, the formation and change of social networks themselves have been the object of many research projects. An important sociological principle is social homophily, which asserts that people tend to form positive relations with others similar to themselves. Actors could be attracted to others based on similarity of attributes—such as gender, age, race, ethnicity, or socioeconomic status—or similarity of behaviors—such as life experiences, political preferences, religious beliefs, or hobby interests. In this perspective, social relations are outcomes, or dependent variables, occurring because actors share some of the independent variables listed previously.
Social network analysis was vitally important to the inception of economic sociology, a major specialty in sociology. In his classical article applying sociology to economic actions, Mark Granovetter (1985) criticized the undersocialized view of economists in which human decision making is driven solely by subjective expected utility maximization. Surprisingly, Granovetter likewise disapproved of the oversocialized view of sociologists in which human actions are determined solely by norms and social roles. So how does one avoid both under- and oversocialized explanations of human behaviors? The answer, quite obviously, is by using social network analysis: by looking at actors’ social networks, we can better understand their decisions and actions. Social networks generate localized norms, rules, and expectations among their members, which reinforce mutual trust and sanction malfeasance. Thus, by examining how social networks actually operate as both causes and consequences of human perceptions and actions, theorists and researchers avoid accepting either oversocialized or undersocialized perspectives. More importantly, although Granovetter (1985) emphasized economic behaviors, his arguments are very relevant to many social pursuits, such as making friends, casting votes, looking for a job, seeking promotion, finding a therapist, searching for emotional support, and locating instrumental help.
Early sociological and anthropological research on social networks inspired other disciplines to investigate the mechanisms instigating network formation in those fields. Over the past half century, mass communication, strategic management, marketing, logistics, public administration, 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
