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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.

      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 jpg

      aThis table is adapted from James Moody with permission.

      Types of Ties

      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).

      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 jpg

      aAdapted from Borgatti et al. (2009).

      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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