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# 17: Two-Mode Networks

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Two-mode data offer some very interesting analytic possibilities for gaining greater understanding of "macro-micro" relations. In the Davis data, for example, we can see how the choices of the individual women "make" the meaning of the parties by choosing to attend or not. We can also see how the parties, as macro structures, may affect the choices of the individual women. With a little creativity, you can begin to see examples of these kinds of two-mode, or macro-micro social structures everywhere. The social world is one of "nesting" in which individuals (and larger structures) are embedded in larger structures (and larger structures are embedded in still larger ones). Indeed, the analysis of the tension between "structure and agency" or "macro and micro" is one of the core themes in sociological theory and analysis. In this chapter we will take a look at some of the tools that have been applied (and, in some cases, developed) by social network analysts for examining two-mode data. We begin with a discussion of data structures, proceed to visualization, and then turn our attention to techniques for identifying quantitative and qualitative patterns in two-mode data.

• 17.1: Introduction to Two-Mode Networks
• 17.2: Bipartite Data Structures
• 17.3: Visualizing Two-Mode Data
There are no new technical issues in using graphs to visualize 2-mode data. Both actors and events are treated as nodes, and lines are used to show the connections of actors to events (there will be no lines from actors to actors directly, or from events to events).
• 17.4: Quantitative Analysis
When we are working with a large number of variables that describe aspects of some phenomenon, we often focus our attention on what these multiple measures have "in common". Using information about the co-variation among the multiple measures, we can infer an underlying dimension or factor; once we've done that, we can locate our observations along this dimension.
• 17.5: Qualitative Analysis
Often all that we know about actors and events is simple co-presence. In cases like this, the scaling methods in the last section can be applied, but one should be very cautious about the results. This is because the various dimensional methods operate on similarity/distance matrices, and measures like correlations (as used in two-mode factor analysis) can be misleading with binary data. Even correspondence analysis, which is more friendly to binary data, can be troublesome when data are sparse.
• 17.S: Two-Mode Networks (Summary)
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.