Modeling the coevolution of relational events and relational states in social networks

Abstract

We present a statistical model for the joint analysis of temporal social network data collected as relational events (e.g., digital traces) and as a series of relational states (e.g., repeated surveys). This model effectively combines behavioral and cognitive measures of social ties. It enables hypothesis tests on the coevolution between digital behavioral traces of relationships (e.g., social interactions online) and their cognitive perceptions (e.g., friendships). We introduce a new estimation routine, based on the expectation-maximization algorithm, to address the challenge posed by observing digital and survey data at different frequencies. We apply this framework in an empirical study of an emerging community of first-year undergraduate students at a Swiss university. We combine repeated survey measures of friendship perceptions with time-stamped social media connections collected through the Facebook API. Our results suggest that the online and offline networks coevolve: changes in one network provide valuable information for modeling the respective other. Additionally, indirect connections in the offline network were associated with the creation of direct ties in the online network. Both networks exhibit similarities in transitivity, homophily, and gender effects. However, they differ in preferential attachment: there is evidence for degree popularity online and against it in the offline network. The paper highlights the broad applicability of the model for future dynamic social network studies that combine relational events and relational states.