By Robert A. Hanneman
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Extra resources for Introduction to Social Network Methods
Each data line is described by it's origin and destination, and value. Here. since there are two relations, "KNOKI" and "KNOKM" there are two values -- each of which happens, in our example, to be binary (but they could be valued). The *Tie properties section is probably best created by using NetDraw and saving the resulting file. Each tie is identified by origin and destination, and it's color and size are set. Here, certain ties are not to be visible in the drawing (the "active" property is set to "FALSE").
West" and "Educ" have very different patterns of ties to the other nodes. 7 and the earlier graphs is that direction may be meaningful. Notice that there is a cluster of nodes at the left (News, Indu, Comm) that are all pretty much not welfare organizations themselves, while the nodes at the right are (generally) more directly involved in welfare service provision. html (12 of 22)7/31/2008 6:13:25 AM Introduction to Social Network Methods: Chapter 4: Visualizing with NetDraw Let me emphasize that different applications of MDS (and other scaling tools) to different definitions of what it means for nodes to be "similar" can generate wildly different looking graphs.
In the Bob, Carol, Ted and Alice example, we noted that two of the actors were male and two female. The scores of the cases (Bob, Carol, Ted, Alice) on the variable "sex" are a nominal dichotomy. It is also pretty common to be able to divide actors in a "multiple-choice" way; that is, we can record an attribute as a nominal polyotomy (for example, if we knew the religious affiliation of each actor, we might record it as "Christian," "Muslim," "Jewish," "Zoroastrian," or whatever). It is often the case that the structure of a network depends on the attributes of the actors embedded in it.