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A new modeling framework for bipartite social networks arising from asequence of partially time-ordered relational events is proposed. We directlymodel the joint distribution of the binary variables indicating if each singleactor is involved or not in an event. The adopted parametrization is based onfirst- and second-order effects, formulated as in marginal models forcategorical data and free higher order effects. In particular, second-ordereffects are log-odds ratios with meaningful interpretation from the socialperspective in terms of tendency to cooperate, in contrast to first-ordereffects interpreted in terms of tendency of each single actor to participate inan event. These effects are parametrized on the basis of the event times, sothat suitable latent trajectories of individual behaviors may be represented.Inference is based on a composite likelihood function, maximized by analgorithm with numerical complexity proportional to the square of the number ofunits in the network. A classification composite likelihood is used to clusterthe actors, simplifying the interpretation of the data structure. The proposedapproach is illustrated on a dataset of scientific articles published in fourtop statistical journals from 2003 to 2012.

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks