Network Connections HD

For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP In this module, we start our discussion on one of the central concepts within network theory that of connectivity where we are interested in analyzing the connections an individual node has. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: In the last section we laid down the basics of our language for talking about networks by looking at graph theory, in this module we are going to continue to expand on our vocabulary but this time focusing on how we model the connectivity of a node or between nodes within a graph. As we mentioned connectivity is a key concept within the network paradigm, we often try to measure the significance of things in terms of the quantity to one of their properties but within networks how connected an individual node is becomes a key metric of its significant within the network. So an important question we will often be asking is how connected is any given node in the network? And this is termed its degree of connectivity. The degree of a node in a network is a measure of the number of connections it has to other nodes. This degree of connectivity can be interpreted in terms of the immediate likelihood of a node catching whatever is flowing through the network, so for example the higher your degree within a given social network the more likely you are to hear about some juicy piece of gossip, because you have many more channels through which to intercept it, of cause this works both ways as it might not be juicy gossip that is spreading on this network, but instead a virus, connectivity is often interpreted as a positive thing but not always. If we are analyzing an undirected and unweight network, a node’s degree of connectivity will then simply be a summation of all the links that the node has. If the graph is directed then we can refine our analysis by dividing this into a measure for the amount of in and out links, so returning to our example of nations trading the in degree of any node would be the total number of import relations it has with other nations and the out degree would be the total number of exporting relations it has. If this is a weighted graph we can then of cause refine our model farther by placing quantitative values on each edge. If an edge exists between node A and B then we say they are adjacent, so if we take a map of a subway we could say that each station or node is adjacent to any other station that is just one stop away from it. We can then capture all the relations within a network by creating an adjacency matrix. This is a non- mathematical introductory course so we will not be going into matrixes but to just give you a quick insight into how this is represent. We can create a simple 2 by 2 matrix to capture the connections within a network by placing a 1 at the cross section between tw