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The two indispensable elements of any social network are entities and relations. Their combination jointly constitutes a social network, as described in the next subsection. Entities may be individual natural persons or collective actors such as informal groups and formal organizations. Common examples of individual actors include children on a playground, high school students attending a prom, employees in a corporate work team, staff and residents of a nursing home, and terrorists operating in a covert cell. Collective actors might be firms competing in an industry, voluntary associations raising funds for charities, political parties holding seats in a parliament, and nations signing a military alliance. Other types of entities lack human agency, such as bills debated in a legislature, dances attended by students, and books read by library patrons. Sometimes networks are comprised of diverse types of entities, such as a healthcare system consisting of doctors and nurses, patients, clinics, hospitals, laboratories, insurance companies, and governmental regulations.

A relation is generally defined as a specific kind of contact, connection, or tie between a pair of entities, or dyad. Relations may be either directed, where one actor initiates and the second actor receives (e.g., advising, selling), or undirected, where mutuality occurs (e.g., conversing, collaborating). A relation is not an attribute of one entity but is a joint dyadic property that exists only so long as both participants maintain their association. An enormous variety of relations among individual and collective entities may be relevant to representing network structures and explaining their effects. At the interpersonal level, children befriend, play with, fight with, and confide in one another. Employees work together, discuss, advise, trust, undermine, and betray. Among collectivities, corporations exchange goods and services, communicate, compete, sue, lobby, and collaborate. In healthcare systems, physicians refer patients to specialty clinics, pharmacies, laboratories, hospitals, imaging centers, nursing homes, and hospices. Which specific type of relation a network researcher should measure depends on the particular objectives of the research project. For example, an investigation of community networks will likely examine various neighboring activities, whereas a study of banking networks would investigate financial transactions. Of course, some analyses scrutinize multiple types of relations, such as the political, social, and economic ties among corporate boards of directors. We present a general classification of relational contents in the next subsection.

Social science researchers rely heavily on measuring and analyzing the attributes of individual or collective units of analysis, whether through survey, archival, or experimental data collection. Although attributes and relations are conceptually distinct approaches to investigating social behavior, they should not be viewed as mutually exclusive options. Instead, many entity attributes can be reconceptualized as relations connecting dyads. For example, a nation’s annual volumes of exports and imports are characteristics of its economy. But, the amount of goods and services exported and imported between all pairs of nations represents the structure of trading networks in the global economy. Patents awarded to scientists employed at high-tech firms indicate companies’ research innovations, but patent-citation networks reveal how knowledge flows through industries (Zhang, Kong, Zheng, Wan, Wang, Hu, & Shao, 2016). The number of friends indicates a child’s popularity, but only network analyses of all dyadic friendship choices can uncover important cliques and clusters. Relations reflect emergent dimensions of complex social systems that cannot be captured by simply displaying a variable’s distribution or averaging its members’ attributes. Structural relations potentially influence both individual behaviors and systemic outcomes in ways not reducible to entity characteristics. For example, efforts to control sexually transmitted infections among injection drug users and sex workers require knowledge of both social and geographic distances among street people. Researchers identified 101 “hotspots” of high-risk activities in Winnipeg, Canada, where “the combination of spatial and social entities in network analysis defines the overlap of vulnerable populations in risk space, over and above the person to person links” (Logan, Jolly, & Blanford, 2016). An experiment in a large environmental nongovernmental organization found that “boundary spanners”—individuals who cross internal boundaries, such as departmental or geographic location, via their informal social networks—were more likely to diffuse innovations, although positions in a formal organizational hierarchy mediated this activity (Masuda, Liu, Reddy, Frank, Buford, Fisher, & Montambault, 2018). The strong inference is that exclusively focusing on actor attributes loses many important explanatory insights provided by network perspectives on social behavior.