8. Logit Models for Affiliation Networks

TLDR

Statistical analysis of network data has expanded rapidly, with new modeling and estimation strategies enabling complex dependency structures, though prior work has focused mainly on one‑mode networks of direct ties between actors. This study aims to extend these models to affiliation networks, where actors are linked indirectly through shared group or event memberships. The authors formulate logit models that estimate the odds of an actor’s participation in an event (or an event’s inclusion of an actor) based on properties of the two‑mode network of actor–event memberships. Illustrative analyses of classic affiliation network datasets demonstrate the applicability of the proposed models.

Abstract

Once confined to networks in which dyads could be reasonably assumed to be independent, the statistical analysis of network data has blossomed in recent years. New modeling and estimation strategies have made it possible to propose and evaluate very complex structures of dependency between and among ties in social networks. These advances have focused exclusively on one-mode networks—that is, networks of direct ties between actors. We generalize these models to affiliation networks, networks in which actors are tied to each other only indirectly through belonging to some group or event. We formulate models that allow us to study the (log) odds of an actor's belonging to an event (or an event including an actor) as a function of properties of the two-mode network of actors' memberships in events. We also provide illustrative analysis of some classic data sets on affiliation networks.