Using the relational event model (REM) to investigate the temporal dynamics of animal social networks

Abstract

Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula, in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

“…We believe that this is a good time for proposing a new method to enrich the toolbox of social scientists for the analysis of network data, given the constantly growing interest in analyzing relational events over the past decade. Recent examples include the study of communicational dynamics of animals (Tranmer et al 2015) and emergency responders (Butts 2008), brokerage and receiver choice in communication networks (Quintane and Carnabuci 2016;Stadtfeld, Geyer-Schulz, and Allmendinger 2011), e-mail communication in organizations (Perry and Wolfe 2013), interaction within teams (Leenders, Contractor, and DeChurch 2016), exchange of patients between hospitals (Kitts et al 2017;Vu et al 2017), and collaboration on online platforms (Vu et al 2011). Further, international conflict relations were analyzed in a relational event framework (Lerner et al 2013).…”

The application of statistical network models in disease research

“…The concept is similar to event models used in survival analysis, and estimates a hazard function for the rate of interaction events conditional on covariates measured on either individuals or events, and also on patterns of these interactions in the past (Tranmer et al . ). Within a ‘relational’ framework, it is possible to additionally estimate coefficients for the influence of network effects on these events such as transitivity – a tendency to interact with ‘ friends of friends ’ (Butts ).…”

“…Relational event models (REMs) provide a modelling framework capable of analysing data on contacts, interactions or associations that have not been aggregated, remain temporally explicit and are instantaneous events without measurable duration (Butts 2008;Tranmer et al 2015). The concept is similar to event models used in survival analysis, and estimates a hazard function for the rate of interaction events conditional on covariates measured on either individuals or events, and also on patterns of these interactions in the past (Tranmer et al 2015). Within a 'relational' framework, it is possible to additionally estimate coefficients for the influence of network effects on these events such as transitivitya tendency to interact with 'friends of friends' (Butts 2008).…”

“…REMs can be fitted in R using the package rem (Brandenberger 2016) or using the package relevent (Butts 2008), with prior data manipulation requiring the package informR (Marcum & Butts 2015). This includes the addition of support constraints (additional binary indicators within the model that restrict which actions or events are possible) that can help account for elements of the study design, and therefore are likely to be particularly beneficial in studies of animals (Tranmer et al 2015). For example, support constraints could inform a model when individuals are collared in a contact network study, or to indicate whether two individuals are on different sides of a geographical barrier (e.g.…”

A Relational Event Approach to Modeling Behavioral Dynamics

“…Recently, relational event models have been used to study phenomena as diverse as reciprocity in food-sharing among birds (Tranmer et al, 2015); social disruption in herds of cows (Patison et al, 2015); cooperation in organizational networks (Leenders et al, 2015); conversational norms in online political discussions (Liang, 2014); and multiple event histories from classroom conversations (DuBois et al, 2013b).…”