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Homophily, the tendency of individuals who are alike to form ties with oneanother, is an important concept in the study of social networks. Yetaccounting for homophily effects is complicated in the context of bipartitenetworks where ties connect individuals not with one another but rather with aseparate set of nodes, which might also be individuals but which are often anentirely different type of objects. As a result, much work on the effect ofhomophily in a bipartite network proceeds by first eliminating the bipartitestructure, collapsing a two-mode network to a one-mode network and therebyignoring potentially meaningful structure in the data. We introduce a set ofmethods to model homophily on bipartite networks without losing information inthis way, then we demonstrate that these methods allow for substantivelyinteresting findings in management science not possible using standardtechniques. These methods are implemented in the widely-used ergm package forR.

Modeling Homophily in Exponential-Family Random Graph Models for Bipartite Networks