The pandemic which began in 2020 set in motion a series of disruptions, or departures from the norm, rendering exceptional aspects of lives which were previously quotidian, and rendering mundane those which were previously novel. To cross a border became a distant memory, while to work from home shifted from novelty to necessity, and then monotony. More crucially, to hear of illness or even death in a neighbouring area or country became not a reason to mourn but a metric by which progress or its lack was marked.

Taiwan itself is a state of exceptions. Although a self-governed and democratic island of 23 million, it is excepted from the contemporary global community which takes the nation-state as a basic unit and building block. Governed by the Japanese from 1895 to 1945, Taiwan was then transferred to the Republic of China (ROC) after World War II. The ROC, a state centred around the person of Chiang Kai-shek, then retreated to its newly acquired island in 1949 after defeat in a civil war with Communist forces. Chiang and his own forces established a China under martial law, an alternative to Mao Zedong's PRC. This repressive state evolved, through much internal struggle and external pressure, into a fledging, and soon fully formed and highly transparent democracy. However, despite its comparatively liberal political situation, Taiwan remains unrecognised as a sovereign state by all but a handful of nations.

This journey has left the Taiwanese people with some exceptions of their own. The threat of invasion by the People's Republic of China has created a cognitive dissonance for the citizenry. Taiwan is at once an heir of traditional Chinese culture and a society in need of protection from the contemporary Chinese state.

The frontiers of network analysis keep expanding with new data sources and new ways to see structure and model relations. Traces of interactions and relations are now constantly streaming and being recorded through social network platforms. New technologies are affording new ways to visualize and analyze massive online data sets, as well as flowing interactions using video and sensor data. These new data sources are being met with new data mining approaches, giving us a deeper and wider view of social structure. Moreover, these new technologies are undoubtedly changing aspects of social structure itself, as people form ties and influence one another in ways that were unimaginable a generation ago. What is missing, we contend, is a systematic way of linking these projects to a theory of social structure (as outlined in Chapter 2). We conclude by proposing three strategies for addressing open problems and moving forward in modeling social structure.

Whereas in one-mode data, individuals or groups are connected directly with one another through interactions or relations, in two-mode data, individuals are indirectly connected with one another through affiliations (events, organizations, associations, alliances, and so on). Affiliation data are often used as a proxy for detecting ties among social actors when direct evidence of ties is difficult to obtain. For example, it is generally easier to know that two people belong to the same club or work in the same department than to know that they have lunch together every Thursday. But affiliation data can also be used to see aspects of social structures not visible in one-mode networks. Duality is a kind of structural relation that shows how levels of social structure intersect with one another. We discuss the classic approach to duality as well as two generalizations that extend the duality approach in hierarchical, temporal, and spatial directions.

When does a collection of individuals become a group or a community? What holds groups, communities, and societies together, even as individuals come and go? These questions concern social cohesion, the bonds through which otherwise disconnected individuals become part of something larger and more lasting than themselves. Social cohesion is perhaps the most central issue in the founding of sociology as a discipline, and its relevance persists today. Social network analysis has much to offer in making the study of social cohesion more formal and precise. Whereas in the previous chapter, we examined structures from the standpoint of their constituent elements of dyads and triads, here we step back to try to see more of the bigger structural picture through the overall pattern of ties in a network.

By looking at networks as collections of smaller elementary structural forms – mainly all combinations of two nodes (dyads) and three nodes (triads) among whom ties may or may not exist – one can learn much about the larger structure. This is especially useful when that structure is very large and therefore difficult to see as a whole. And yet, these most elementary forms of social structure are not simply mathematical constructs; they reflect the fundamental ways that social actors relate with one another as individuals and as social units (i.e., sociality). Thus, a network with many social elements of one type, and fewer of another, suggests a certain way of relating involved in how the network has formed and where it might be going. In this chapter, we introduce the reader to dyads and triads as forms of interacting and relating. We cover techniques for decomposing networks into these constituent elements and connecting variation at the micro level as a way of seeing macro-level structures.

The primary aim of social network analysis is building and evaluating theories of social structure – that is, enduring patterns of human interaction and ways of thinking about and organizing human groups. The sheer complexity of social structure prevents encapsulation in any single model, and this complexity is compounded as we incorporate cultural beliefs and social expectations in addition to interactions. Networks link actors to one another in systems, raising tricky questions about the locus of control and activity, particularly regarding the extent to which people are active agents or passive puppets (to put it bluntly) of social structure. While acknowledging deep and ultimately unsettled issues in the field, we provide readers with an overarching though still evolving theoretical account of social structure that can guide both inductive and deductive social network research and allow plug-in points for different perspectives on agency, culture, and constraint.

We outline key conceptual issues and strategies in social network data collection, focusing on the differences between realist and nominalist approaches. Given that most networks are incomplete in some way, we discuss ways to anticipate and assess problems with missing data.

Some people take orders all day. Others give them. And most people are somewhere in the middle. While relations of “who orders whom” are generally established through formalized hierarchies of authority, informal relations such as business partnerships and even friendships are also frequently hierarchical in some way: some business partners have more control over important resources, some friends have more clout. Indeed, status and reputation structure almost all areas of social life. To understand social structure, we must attend to both horizontal relations in which individuals are connected through frequently mutual feelings of belonging, as well as vertical relations of power, authority, deference, and status that are asymmetric. Ultimately, how community and hierarchy combine is one of the most vexing concerns in the social sciences. Building on the previous chapter’s focus on groups and cohesion, this chapter focuses on aspects of social structures that are more asymmetric, centralized, or hierarchical.

Connectionist approaches to social networks often speak of flows of ideas, attitudes, and behaviors through ties as social influence and as peer influence in the specific case of flows among friends and acquaintanceships. Modeling social influence is no easy task. How do we determine where a particular idea came from in a network and who influenced whom? In establishing the presence of social influence, a researcher must theoretically and empirically address many potentially confounding factors and alternate explanations. In the previous chapter, we covered network approaches to generic flows at scale. In this chapter, we more thoroughly cover some of the thorny issues involved in tracing interpersonal influences and key modeling strategies in obtaining more detailed views of what flows and to whom.

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.