2.1 Underlying Assumptions
The network perspective emphasizes structural relations as its key orienting principle. Siegfried Nadel, the great British anthropologist, proposed a relational definition of social structure: “We arrive at the structure of a society through abstracting from the concrete population and its behaviour the pattern or network (or ‘system’) of relationships obtaining ‘between actors in their capacity of playing roles relative to one another’” (Nadel, 1957, p. 12). By network, he meant “the interlocking of relationships whereby the interactions implicit in one determine those occurring in others” (p. 16). By separating structural forms from their empirical contents, structural analysts can uncover the underlying systems of roles that arise from interdependent activities of the persons performing those roles. Nadel further contributed to nascent network science by suggesting that matrix methods could graphically depict network relations. Nadel’s conceptualization of networks as relational social structures was widely adopted by social network theorists and researchers over the ensuing decades of development. For example, Harrison White and his colleagues defined social structure as “regularities in the patterns of relations among concrete entities; it is not a harmony among abstract norms and values or a classification of concrete entities by their attributes’’ (White, Boorman, & Breiger, 1976, pp. 733–734). More recently, the core mechanisms in Crossley and Krinsky’s (2016) relational approach to sociology are interactions, relations, and networks. In network analyses, the entities may be individual natural persons, small groups, organizations, or even nation-states. Some types of network entities lack agency, such as documents posted on websites and participatory events such as sports matches and social movement protests. The patterns of relations connecting members of one or more sets of entities comprise the macrosocial contexts, or overall relational structures, that influence actor perceptions, attitudes, beliefs, decisions, and actions. The primary objectives of network analysis are to measure and represent these structural relations accurately and to explain both why they occur and what their consequences are.
Social network analysis rests on three underlying assumptions about structural relations and their consequences. First, structural relations are often more important for understanding observed behaviors than are such characteristics as race, gender, age, socioeconomic status, and political ideology. For example, research on voting behavior and social movement participation found that egocentric network structures more strongly influence people’s choices than respondent attributes (Diani, 2004; Huckfeldt & Sprague, 1987; Knoke, 1990). Many actor attributes remain unaltered across the numerous social settings in which they participate (a woman’s age, race, and education remain unchanged whether at home, at work, and at church). In contrast, many structural relations occur only at specific time-and-place locales and either vanish or are suspended when participants are elsewhere (e.g., student-teacher and doctor-patient relations do not exist outside school and clinic settings, respectively). A man holding a menial factory job requiring little initiative may be the dynamic leader of his church and an enthusiastic softball team player. Such behavioral differences are difficult to reconcile with unaltering gender, age, and status attributes but comprehensible on recognizing that people’s structural relations can vary markedly across social contexts within which they are embedded. The structural-relational explanations favored by network analysts depart markedly from substantialist approaches premised on static ‘‘thing-concepts’’ as their primary units of analysis: essences, self-action, norm-based conformity, rational choice, and variable-centric and social identity approaches (Emirbayer, 1997). In assuming that patterned relations influence social entities apart from their attributes, network analysis offers distinctive theoretical and empirical explanations of the origins of social action.
Second, social networks affect actor perceptions, beliefs, and actions through diverse structural mechanisms that are socially constructed by relations among entities. Direct contacts and more-intensive interactions dispose people and organizations to be better informed, more aware, and more susceptible to influencing or being influenced by others. Indirect relations through intermediaries (in popular imagery, agents who broker connections for their clients) also bring exposure to new ideas and potential access to useful resources that may be obtained through exchanges with others. For example, in a classic network study by Mark Granovetter (1973), job seekers typically obtained less useful information from their intimate circles, whose members already shared and circulated the same intelligence, than from their weaker and more distant social contacts. Relational structures provide complex pathways for assisting or hindering flows of knowledge, gossip, and rumor through a population (Fang, McAllister, & Duffy, 2017). A variety of structural-relational factors explains racial differences in the spread of HIV/AIDS infections among young men who have sex with men (Mustanski, Birkett, Kuhns, Latkin, & Muth, 2015) and the propagation of financial distress through the international banking network during the global financial crisis of the aughts (Kojaku, Cimini, Caldarelli, & Masuda, 2018). Physical illness, mental health, and recovery from substance abuse are strongly affected by people’s social support networks (Cullen, Mojtabai, Bordbar, Everett, Nugent, & Eaton, 2017; Stevens, Jason, Ram, & Light, 2015), with social media exerting some unusual impacts (Lu & Hampton, 2017; Pallotti, Tubaro, Casilli, & Valente, 2018). Structural relations are vital to building cohesion and solidarity within a group but may also reinforce prejudices and intensify conflict with out-groups (Bliuc, Faulkner, Jakubowicz, & McGarty, 2018; Roversi, 2017). Competitive and cooperative relations enable innovation in corporate supply chains (Delgado-Márquez, Hurtado-Torres, Pedauga, & Cordón-Pozo, 2018), mobilization for collective action by social movements (Diani, 2016), and the operation of ‘‘dark networks’’ for drug trafficking, immigrant smuggling, and terrorist campaigns (Wu & Knoke, 2017). By channeling information, money, and other types of resources to particular structural locations, networks help to create interests and shared identities and to promote shared norms and values. Network analysts seek to uncover the mechanisms through which social relations affect social entities and to identify the contingent conditions under which particular mechanisms operate in specific empirical contexts.
The third underlying assumption of network analysis is that structural relations should be viewed as dynamic processes. This principle recognizes that networks are not static structures but are continually changing through interactions among people, groups, or organizations. In applying their knowledge about networks to leverage advantages, network entities also transform those structural relations, both intentionally and unintentionally. For instance, in an intervention experiment to reduce conflict and bullying among students in 56 schools, experimenters comprehensively measured every school’s networks, then randomly selected “seed groups” of 20 to 32 students to be encouraged to take public stands against conflict (Paluck, Shepherd, & Aronow, 2016). Disciplinary reports of conflict fell by 30% in the treatment schools compared to control-group schools, but the effect was stronger for seed groups containing more students who attracted greater student attention. Apparently, those popular students changed their network peers’ beliefs and behaviors by publicly stigmatizing conflict and bullying as less socially normative. Such dynamics exemplify the more general ‘‘micro-to-macro problem’’ in the theory of social action (Coleman, 1986). The core issue is how large-scale systemic transformations emerge out of the combined preferences and purposive actions of individuals. Because network analysis simultaneously encompasses both structures and entities, it provides conceptual and methodological tools for linking changes in actors’ microlevel choices to macrolevel structural alterations. The increased availability of longitudinal datasets, especially large online networks, coupled with methodological developments for analyzing multilevel relations, are accelerating research on cross-level dynamic processes (Lazega & Snijders, 2015; Snijders, Steglich, & Schweinberger, 2017). Likewise, developments in temporal exponential random graph models (TERGMs) and stochastic actor-oriented models (SAOMs), such as SIENA, hold great promise to advance our understanding of network dynamics (Leifeld & Cranmer, 2019; Leifeld, Cranmer, & Desmarais, 2018).
2.2 Entities and Relations