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So, just throwing in a mixture or throwing in a latent dimension, to me, kind of misses the point of why do people form partnerships with others. So, when I go into this, I go in saying, I want to add attributes to this. A lot of people who have worked in the network field don’t think attributes matter because somehow it is still at the respondent level. We all know that, as good network analysts, we don't care about individuals. We only care about network properties and dyads.

DR. SZEWCZYK: We care about individuals.

DR. MORRIS: Attributes do a lot of work. They do a lot of heavy lifting in these models and they actually explain, I think, a fair amount. I would call it model degeneracy only in this case because you get an estimate and you might not even realize it was wrong. In fact, when people used the pseudolikelihood estimates, they had no idea that the cute little estimate they were getting with a confidence interval made no sense at all. It is degenerate because what it does, it performs the function. It actually gets the average right, but then it gets all the details wrong. So, you can call that inadequate, and it is. It is a failure. It is a model failure. That is very clear.

DR. WIGGINS: So, one thing to follow up on that I was wondering about, since each of these models defined a class, I wonder if you thought about treating this using not classifiers, large-margin classifiers, like support vector machines. Some anecdotal evidence is that sometimes you can tell if none of your network models is really good for a network that you are interested in. So, some of these techniques that measure everything at once, rather than measuring a couple of features you want to reproduce will show you how one network, if you look at it in terms of one attribute, it looks like model F, but if you look at it in terms of a different model, it turns out to be model G, and that might be one way of seeing whether or not you have heterogeneity or just none of your models is a good model. If you have a classifier, then all the different classifiers might choose, not me, as the class, in which case you can kind of see if none of your models is the right model.

DR. MORRIS: Yes, that is a nice idea.



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