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

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