Abstract
Analyzing two-mode networks linking actors to events they attend may help to uncover the structure and evolution of social networks. This classic social network insight is particularly valuable in the analysis of data extracted from contact diaries where contact events produce — and at the same time are the product of relations among participants. Contact events may comprise any number of actors meeting at a specific point in time. In this paper we recall the correspondence between two-mode actor–event networks and hypergraphs, and propose relational hyperevent models (RHEM) as a general modeling framework for networks of time-stamped multi-actor events in which the diarist (“ego”) simultaneously meets several of her alters. RHEM can estimate event intensities associated with each possible subset of actors that may jointly participate in events, and test network effects that may be of theoretical or empirical interest. Examples of such effects include preferential attachment, prior shared activity (familiarity), closure, and covariate effects explaining the propensity of actors to co-attend events. Statistical tests of these effects can uncover processes that govern the formation and evolution of informal groups among the diarist’s alters. We illustrate the empirical value of RHEM using data comprising almost 2000 meeting events of former British Prime Minister Margaret Thatcher with her cabinet ministers, transcribed from contact diaries covering her first term in office (1979–1983).
Highlights
Interpersonal relations are shaped by, and at the same time shape membership of actors in informal groups (Breiger, 1974)
In this paper we recall the correspondence between two-mode actor–event networks and hypergraphs, and propose relational hyperevent models (RHEM) as a general modeling framework for networks of timestamped multi-actor events in which the diarist (‘‘ego’’) simultaneously meets several of her alters
Building on the framework proposed in Lerner et al (2019), in this paper we develop relational hyperevent models (RHEM) for networks of time-stamped multi-actor events transcribed from contact diaries
Summary
Interpersonal relations are shaped by, and at the same time shape membership of actors in informal groups (Breiger, 1974). Longitudinal studies using time-stamped relational event data are increasingly adopted in the political sciences (Lerner et al, 2013a; Stadtfeld et al, 2017; Brandenberger, 2019) – but models are typically for dyadic events and do not directly apply to multi-actor events transcribed from contact diaries. This is a severe limitation for political science since appointment diaries exist widely and contain masses of information about the working lives of individuals, leaders and managers. Such data makes it possible to study the British core executive using advanced network analytic methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
