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Further, you can see whether or not homophily holds after accounting for other network properties. Do you keep running with different parameters until you get good diagnostics? For example, I may want to scale down the size of the network so that I can run computational extensive algorithm on the random graphs with similar statistical characteristic as the observed network. And several stem from the same dyad. It's color-coded for your convenience!
Basically, it will result in an overly dense random graph. The example here compared different types of homophily against each other, identifying which are more meaningful than others and how they operate. Gabriel Actually I was really hoping for an answer to this one. That will provide more information on the robustness of the simulations.
Many other network properties could be tested alongside homophily, though. That could be done by just looking at the observed network. Print it out and reference during the show, or give to friends who've just started watching. Homophily is just one family of mechanisms. In this case, fixed-point method for validating the first-order approach resources the other network property examined was monogamy degree of one.
How is this useful, what would you do with this model in practice?
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