Abstract
Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge - if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step towards integrating these two perspectives by introducing a social space diffusion model. In the model, network ties indicate positions in social space, and diffusion occurs proportionally to distance in social space. Practically, the simulation occurs in two parts. First, positions are estimated using a statistical model (in this example, a latent space model). Then, second, the predicted probabilities of a tie from that model - representing the distances in social space - or a series of networks drawn from those probabilities - representing routine churn in the network - are used as weights in a weighted averaging framework. Using longitudinal data from high school friendship networks, I explore the properties of the model. I show that the model produces smoothed diffusion results, which predict attitudes in future waves 10% better than a diffusion model using the observed network, and up to 5% better than diffusion models using alternative, non-model-based smoothing approaches.
Highlights
Social networks have historically been used for two different purposes
Social networks represent the paths that contagions—like innovations such as the use of a new medicine (Coleman, Katz, and Menzel 1966), information such as job opportunities (Granovetter 1973, 1995), diseases such as human immunodeficiency virus infection (Morris et al 2009), or even a medical condition such as obesity (Christakis and Fowler 2007; but see Lyons 2011)—take to spread through a population
The dual nature of networks as stable connections and as random draws from social space creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion, or the spread of something through a connected group of people, only follows the observed network ties? Under experimental conditions, in which online network ties are created by the researcher (e.g., Centola 2011), it is reasonable to assume that diffusion can only follow the given network ties
Summary
Social networks have historically been used for two different purposes. First, social networks represent the paths that contagions—like innovations such as the use of a new medicine (Coleman, Katz, and Menzel 1966), information such as job opportunities (Granovetter 1973, 1995), diseases such as human immunodeficiency virus infection (Morris et al 2009), or even a medical condition such as obesity (Christakis and Fowler 2007; but see Lyons 2011)—take to spread through a population. Social networks represent the relative positions of people to one another—for example, status (Rossman, Esparza, and Bonacich 2010), hierarchy (Martin 2002), popularity (Moody et al 2011), informal peer group membership (Newman 2006), role structures (White, Boorman, and Breiger 1976), or levels of intergroup contact (Smith, McPherson, and Smith-Lovin 2014) Under the former framework, network ties indicate stable conduits for social influence or information; under the latter, network ties are a random draw from an underlying social space. This approach seeks to integrate two potentially contradictory views of networks, stable connections and random draws, which I will refer to as the “connectionist” and “positionist” viewpoints, respectively (Borgatti and Foster 2003)..
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