A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict

TL;DR

This paper introduces a mixed effects model for analyzing longitudinal relational data, capturing dependencies over time and between actors, with applications to international trade and conflict datasets.

Contribution

It extends social relations models to a stochastic process framework, allowing for intra- and inter-temporal network dependency analysis.

Findings

Effective modeling of international trade networks over time

Application to militarized disputes reveals temporal dependency patterns

Model captures complex relational dynamics in longitudinal data

Abstract

The focus of this paper is an approach to the modeling of longitudinal social network or relational data. Such data arise from measurements on pairs of objects or actors made at regular temporal intervals, resulting in a social network for each point in time. In this article we represent the network and temporal dependencies with a random effects model, resulting in a stochastic process defined by a set of stationary covariance matrices. Our approach builds upon the social relations models of Warner, Kenny and Stoto [Journal of Personality and Social Psychology 37 (1979) 1742--1757] and Gill and Swartz [Canad. J. Statist. 29 (2001) 321--331] and allows for an intra- and inter-temporal representation of network structures. We apply the methodology to two longitudinal data sets: international trade (continuous response) and militarized interstate disputes (binary response).