One of the major continuing themes of social network analysis is the way in which individual actors "make" larger social structures by their patterns of interaction while, at the same time, institutional patterns shape the choices made by the individuals who are embedded within structures.
Two-mode data (often referred to as "actor-by-event" or "affiliation" in social network analysis) offer some interesting possibilities for gaining insights into macro-micro or agent-structure relations. With two-mode data, we can examine how macro-structures (events) pattern the interactions among agents (or not); we can also examine how the actors define and create macro structures by their patterns of affiliation with them. In addition, we can attempt to describe patterns of relations between actors and structures simultaneously.
In this chapter we briefly examined some of the typical ways in which two-mode data arise in social network analysis, and the data structures that are used to record and manipulate two-mode data. We also briefly examined the utility of two-mode graphs (bipartite graphs) in visualizing the "social space" defined by both actors and events.
Our primary attention, though, was on methods for trying to identify patterns in two-mode data that might better help us describe and understand why actors and events "fit together" in the ways they do.
One class of methods derives from factor analysis and related approaches. These methods (best applied to valued data) seek to identify underlying "dimensions" of the actor-event space, and then map both actors and events in this space. These approaches can be particularly helpful in seeking the "hidden logic" or "latent structure" of more abstract dimensions that may underlie the interactions of many specific actors across many specific events. They can also be useful to identify groups of actors and the events that "go together" when viewed through the lens of latent abstract dimensions.
Another class of methods is based on block modeling. The goal of these methods is to assess how well the observed patterns of actor-event affiliations fit some prior notions of the nature of the "joint space" (i.e. "core-periphery" or "factions"). To the extent that the actor-event affiliations can be usefully thought of in these ways, block models also then allow us to classify types or groups of actors along with the events that are characteristic of them.
Two-mode analysis of social networks need not be limited to individual persons and their participation in voluntary activities (as in the cases of our examples, and the original Davis study discussed at the beginning of this chapter). The tools of two-mode analysis could be applied to CSS (cognitive social structure) data to see if perceivers can be classified according to similarity in their perceptions of networks, simultaneously with classifying network images in terms of the similarity of those doing the perceiving. Units at any level of analysis (organizations and industries, nation states and civilizations, etc.) might be usefully viewed as two-mode problems.