Okay! the extension I wanna talk about today is the network extension. We already played around with a little bit in unit 4. But I mentioned a little bit about how you could add a calculation that would be much harder to calculate in that logo but putting then into extensions.And this model really exemplifies that. That's why I wanna this talk about it So, this is another viral marking model of... you know... it's ..in neuro we cal it diffusion information. This is taking into the the point - "I wanna find who is the best person to see... what particular messages if I want that message to diffuse quickly thorugh .. the system. and I have a budget, so, I can determine how many different people I can see.. I have a different types of networks named in preferential attachment like we saw before. But now,I am also gonna have a setting that isn't random or based upon betweenness and betweenness is exactly what I am talking about when I am saying sometime it's better to have a particular calculation umm.. be written in another language and then just have netlogo use an extension to call it. And that's because betweenness is a really complex calculation. What betweenness is calculating .. is it's calculating how often a node appears on the shortest path between any of the other two nodes in the entire network.right.. that involves comparing every node in the network, finding each shortest path to every other node in the network and then figuring out. which ..how many ...and what nodes are on those paths. As you might imagine as we increase the size of the network this calculation absolutely explodes and in many cases we don't waana put code like that directly into a net logo model.right... Instead, it's better to, spend some time, develop a Java vesrion of that.. another language version of that particular code and then have net logo simply call that version on the particula err.. umm.. dataset that we are looking at. That's exactly what the model does.So, what it does is set up network