Well folks, so let's take a quick look back at what we've learned in this weeks lectures. So, in terms of growing random networks, what we saw was beyond just providing a realism in terms of different nodes being born at different times. What they did was provide a heterogeneity based on age which allowed older nodes to have higher degrees. Younger nodes to have relatively lower degrees. And so it gave us an increased difference in the degrees of different nodes. So it started to give us fat tails and, in particular, if we went all the way to the extreme of preferential attachment. Where we now add in the fact that when a node is born it wants to connect to better connected nodes. Then we got extreme versions of fat tails, Power laws, and that begins to give us an idea of why we might be seeing. Or how we might end up with very fat tails in a degree distribution. Now, many networks empirically lie between two extremes, so we looked at some models. That allow people when they're forming links to form some uniformly at random. And then some by searching neighborhoods of those nodes, and finding and needing new people. Connecting to them, and through this combination process we end up with something which is in between, and spans in between. And relatively depending on how much randomness there is, in terms of uniform and how much is, is done through searching the network. We end up with whole series of different degree distributions which allow us to begin to fit different models. So this, this hybrid aspect allows us then to do then to do some estimation and see what might be going on. And we see that, you know, some networks are formed more by searching, and others not. And, and this is quite natural if you've ever done a bibliography search, for instance, if you're searching for references? You might poke around and, and randomly come across things. But then you also when you find an article that seems to be relevant, you'll search through its reference list to find other articles. And so finding the ones that it cited, can be very useful. And so that would be one explanation for why you tend to see fat tables in, in citation networks. looking beyond just the fact that you, things that are better cited are easier to find. They're in fact easier to find through other articles that you're citing and, and that process then leads also to things like clustering and so forth. Last thing we talked about was a, another class of, of models for doing statistical estimation. Exponential Random Graph models. They're flexible, they capture a lot of different things. We saw that they have challenges in estimation. And in particular, the, calculating the relative probabilities is difficult by the sheer number of networks that can be out there. And so there's new techniques. There's variations on these models where instead we work directly in a statistics base. And those things can actually be estimated fairly quickly. And there will be new software coming out. And we'll possibly take a look at those in our exercises. So what we've done is gotten some feeling for the different random Network models. The ones that are out there, how they explain different things. And now what we're going to be doing going forward is, is next looking at Strategic models and then eventually looking at behavior.