  Homophily, the tendency of individuals who are alike to form ties with one
another, is an important concept in the study of social networks. Yet
accounting for homophily effects is complicated in the context of bipartite
networks where ties connect individuals not with one another but rather with a
separate set of nodes, which might also be individuals but which are often an
entirely different type of objects. As a result, much work on the effect of
homophily in a bipartite network proceeds by first eliminating the bipartite
structure, collapsing a two-mode network to a one-mode network and thereby
ignoring potentially meaningful structure in the data. We introduce a set of
methods to model homophily on bipartite networks without losing information in
this way, then we demonstrate that these methods allow for substantively
interesting findings in management science not possible using standard
techniques. These methods are implemented in the widely-used ergm package for
R.
