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.

翻译：同质性（homophily）——即相似个体倾向于相互建立联系的现象——是社会网络研究中的一个重要概念。然而在二分网络背景下，同质性效应的解释变得复杂：二分网络中的连接并非个体之间的直接联结，而是个体与另一组节点之间的联结，后者虽然可能也是个体，但往往是完全不同类型的对象。因此，大量关于二分网络同质性效应的研究首先通过消除二分结构将双模网络压缩为单模网络，从而忽视了数据中潜在的重要结构。我们提出了一套无需通过此种方式损失信息即可对二分网络进行同质性建模的方法，并证明这些方法能够在管理科学领域获得使用标准技术无法得到的实质性有趣发现。这些方法已在广泛使用的R语言ergm包中实现。