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linked to capturing static snapshots of network data, where such temporally specific methodological considerations require unique adaptations or strategies of their own for network research.¹⁴

      ¹⁴ See especially the section in Chapter 3 on “Complex Networks.” As was the case in several areas mentioned above, these will assume you know and work with existing “best practices” from social research methods generally and will only focus on providing description of where network research differs from, or requires additional considerations to, these standard approaches.

Table 1.1 combines the ideas from above into a set of representative questions commonly found in networks research. The list in Table 1.1 is by no means comprehensive but covers a broad sampling from existing research questions. Before we can continue with the central aim of this book to describe methods for capturing the networks that will allow you to examine these types of research questions, we must next address the types of ties that could potentially be measured within any such study.¹⁵

      ¹⁵ In the chapters that follow, I draw on examples that stem from a variety of disciplinary and topical areas. However, I do not organize these sections by those areas, as most of the principles I discuss cut across those domains. As you think about the theoretical motivations for your own work, however, you may find such disciplinarily organized discussion useful. Chapter 6 in Robins (2015) provides a useful broad sketch of a number of these possibilities.

      Table 1.1a

      ^aThis table is adapted from James Moody with permission.

      Types of Ties

In principle, anything that could be represented as a graph could be considered a network and analytically could be examined with SNA. This has often been the practice in the physical and biological sciences (Borgatti et al., 2009), and some in the social sciences have even argued that as one of the beauties of social network analysis—that regardless of the type of nodes or ties between them—the analytic principles can be applied to any network in much the same way (Wellman, 1988).¹⁶ However, just because different networks can be analyzed with the same approaches doesn’t mean they necessarily should be. Different network types could lead to different applications of the same descriptive concepts; many core network ideas (e.g., centrality or communities) have multiple alternate strategies for their measurement, and it’s often easiest to select between those based on differences between the types of networks being described.¹⁷ Moreover, the theoretical mechanisms that provide accounts for different explanatory expectations within networks differ substantially depending on the type of network being examined (Erikson, 2013; Fuhse, 2019, Valente & Pitts, 2017). In either case—and as with any solid social science research—the aims of a network study (whether descriptive, explanatory, or otherwise) must carefully consider what the research aims to address in order to determine what sorts of data will allow them to best examine those questions. Here, we must consider what type(s) of ties the questions are about, how readily researchers can actually capture the types of ties required by their research questions, and whether they will be limited to some sort of proxies for the relationships of actual interest.

      ¹⁶ My former PhD advisor has even been accused at times of literally seeing everything as a network. At a recent workshop we both contributed to, he was the primary person making that accusation.

      ¹⁷ For a review of various strategies for conceptualizing and operationalizing the differences between centrality measures, see Borgatti and Everett (2006); for a similar treatment of network communities, see Fortunato (2010) and Porter, Onnela, and Mucha (2009).

Borgatti et al. (2009) provide a typology of the types of ties that are frequently the focus of social network research;¹⁸ for a summary, see Table 1.2. They differentiate between three primary types of ties: social relations, interactions, and flows.¹⁹

      ¹⁸ The terms relationship and tie are often used more or less interchangeably in the social networks literature. I will attempt to avoid this unnecessary confusion, aiming to use tie as the “catch-all” term and relationship in the specific meaning provided here (see also Erikson, 2013; Kitts, 2014). In leaning on examples from others, I may occasionally slip into the literature norm of also using relationship as the generic term.

      ¹⁹ See Figure 3 in Borgatti et al. (2009). Their typology also includes a fourth type that I will not address in this book: similarities. These are merely dyadic comparisons of some individual attribute (e.g., same gender). While similarities are dyadic measures, they are not conceptually relational by nature. As such, their measurement and modeling are not captured any better by network approaches than by individually oriented research methods and analytic strategies. Similarities are often useful in the analytic modeling of social networks. However, since measuring similarities do not rely on any uniquely network approaches, I leave you to other research methods texts for optimizing their capture.

      Table 1.2a

      ^aAdapted from Borgatti et al. (2009).

Social relations capture the various relationally defined positions a person can occupy with respect to others; these often have a strong social basis and/or foundations in theoretical social science literature. Role theory asserts behavioral expectations upon occupants of certain roles (e.g., parents should behave in particular ways toward their children). Recognizing the relational basis of those roles allows us to identify how these expectations derive from the pattern of relationships that define the role, rather than a more essentialist notion of role expectations determined from the label itself. For example, a parent’s role is determined by the kinship ties they have to their co-parent, their children, and often even their own parents.²⁰ In social relationship terms, a role is defined by the constellation of these others to whom the person is connected. Such kinship relations have been the focus of relational social scientists for decades (Bott, 1957; Stack; 1974, D. R. White & Jorion, 1992; H. C. White, 1963). In addition to kinship ties, Borgatti et al. (2009) describe other social relations that are based on other roles (e.g., friends), affective relationships (e.g., likes/dislikes), or cognitive links (e.g., knows the work of). This variety of social relationships shares a number of common features that make their measurement more readily available—they are generally relatively temporally stable ties, and each member of the relationships can generally readily identify both members’ participation in the relationship. This makes gathering relational information about such roles easily incorporated into a survey-based research design by simply tacking such questions onto individually oriented surveys.

      ²⁰ The “Category” label in Table 1.2 should not be interpreted as indicating those differences only apply to the row specified (e.g., interactions can be subjective or objective, and relations can be mutual or directed), but these are primary delineations on the types of ties that are often the focus of research in these domains (e.g., the perception vs. reality of diffusion [knowledge vs. information]).

      Social interactions capture the joint participation by pairs of nodes in shared activities. The types of interactions that are most commonly studied are things like sent and received messages, engaging in sexual intercourse, the joint use of injecting drug equipment (e.g., needles),

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