Is culture the glue that holds the social structures of society together? Or are there “culture wars” that fundamentally divide us? Clearly, the answer is somewhere in the middle, and trying to understand precisely how culture and social structure interrelate to unite or divide remains a core sociological endeavor. Social network analysis alone cannot resolve such an enormous puzzle, but its methods provide important tools for formalizing a jointly structural and cultural approach to studying society. In this chapter, we conclude Part II on Seeing Structure by outlining efforts to see dualities in the connections between structure and culture – that is, to study how enduring patterns of interaction interrelate with shared understandings, tastes, meanings, and other attitudinal measures. We also discuss the structural analysis of meanings themselves and the application of social network techniques to cultural phenomena.

Social structure is enacted by individuals. At the same time, social structure channels individuals into opportunities for action and provides schemas for helping them make sense of these actions. Structure is therefore both the medium through which individuals realize fundamental human drives as well as the collective outcome of the actions that others take and have taken in the past. This ongoing interplay of agency and structure is called structuration. While predictive models outlined in Part III test specific structuration mechanisms, here we cover more inductive approaches and present various micro-level ideas about what drives people to form and break (certain types of) ties. We then introduce the reader to ego-centric network analysis as an important technique that illuminates many of these structuration processes with individual-level data.

We outline the core concepts of network analysis, issues of research design, and data collection for different structural questions.

Having lived through a global pandemic, or more trivially, having seen online memes “go viral,” we are all intuitively familiar with the spread of things through network ties. Diseases, memes, used books, and cash are ready examples of things passed from one person to another. Somewhat less familiar, perhaps, is that a fundamentally similar mechanism underlies many of our social behaviors. Understanding such processes is therefore related to understanding how anything – information, rumors, diseases, and so on – diffuses through a system. Key questions include: How does a network structure as a whole (its topology) affect the diffusion process? And how does a node’s position in this structure affect the likelihood of transmitting and receiving flows?

Whereas previous chapters have focused on networks as conduits through which important resources and influences flow, this chapter provides a more in-depth account of the positional approach to networks. In doing so, we move away from conceptualizing social structures as more or less cohesive and integrated groups, cliques, communities, etc., toward a view of social structures as comprised of role structures. To use the baseball analogy, in moving toward a more positional view of networks, we shift from seeing teams as interacting individual players with relations with one another to seeing players as enacting the game through an interrelated set of positions on the field that come with role expectations. Thus, as depicted in our view of social structure in Figure 2.3, we begin to move upward and to the right – that is, toward higher levels of structure and greater levels of conceptual abstraction. Doing so requires a different set of methods, which we introduce in this chapter.

Images can be powerful; and, as the saying goes, “with great power comes great responsibility.” Today, the world is suffused with images through various media, and people have come to expect pictures to tell them stories. With increased computational power, images of quantitative data are increasingly part of the “stories” one commonly sees and are powerful in communicating research findings. Many of these images are informative and effective; others are confusing, convey little actual information, or, sadly, are used to intentionally mislead for ideological reasons. Network science has always used compelling images to tell stories about structures, and the field is therefore particularly suited to make the most use of this era of data visualization. But given the vastly expanded palette of visualization available today, how does the researcher decide what is a good network image?

Where do networks come from? Numerous theories direct us to the causes of networks (e.g., homophily, triadic closure, physical proximity), some emphasizing outside factors (exogenous causes) and others emphasizing point-in-time network structure (endogenous causes) as shaping a network’s future trajectory. So far, we have examined such causal theories using cross-sectional snapshots in the form of metrics (centrality, density), partitions (clusters), and maps or spaces (visualization). These approaches generally suffer from a lack of stochastic features and observational overdetermination: for example, we observe a pattern in a given school on a given day, but that pattern could result from actor preferences and constraints in the setting. Disentangling such effects requires an inferential approach to probabilistically examine various effects. To the extent that we want to identify causal forces shaping the networks, understanding the unfolding of relations in time – how the individual ties in a network (the dyads joined by one or more relations) and the entire structure of these relations emerge and evolve – is crucial for testing network theories.

Stop. Take a moment to look around. What do you see? No matter where you are, you are likely perceiving a world consisting of things. Maybe you are reading this book in a coffee shop, and if so, you probably see people, cups, books, chairs, and so on. You see a world of objects with properties, yourself included: white cups are on wooden tables, people sitting in chairs are reading books and talking with one another. At the same time, you are a subject, responding to this world and actively bringing yourself and these objects into interrelation. And yet, the world of objects with properties that you are perceiving is but one slice of a complex reality.