We have already defined the degree of the node as the number of edges connected to it. Thus specifying if a node is more connected than another. Looking closer we can separate out that two components denotes degrees into in degree and out. Which are the counts of the incident and the outgoing edges of a node respectively. In the example graph G has an indegree and outdegree of three making the total degree equal to six. We first construct this degree table for each node. It's a simple procedure where we count the number of nodes with degree. 0, 1, 2, and so forth. The degree versus count table is a degree histogram of the graph. We can compare two graphs by computing the vector distance between them. One simplistic measure is just the Euclidean distance. For our case, the degree histogram based on comparisons of the histograms find the graphs to be very similar. The more sophisticated methods are available but are outside the scope of this course. We can also compute histograms of just the in degree or just the out degree of the graph. But perhaps more interesting is the joint two dimensional histogram of the graph, the colorful histogram of the graph can be interpreted here as follows. The graph has a maximum in degree of three and a maximum out degree of three. This creates a two-dimensional histogram with four times four equal to 16 different joined values. The actual value for any combination is computed from the graph and color coded in the ratings. For example, there is no node with in degree 0 and out degree 0. So the lower-left square of the graph has value zero and color-coded blue. On the other hand, there are two nodes with in-degree three and out-degree three. Thus, the top-right corner has the value twp, which is 20% of the nodes. Color coded as light green. The 2D histogram provides an interesting insight. The nodes with more incident edges than outgoing edges represent entities that take in more than they put out. In a social networking setting, they represent members who are listeners. They receive a lot of posts, but send much fewer posts. On the opposite side of the spectrum, there are talkers whose out-degrees exceed their in-degrees. The entities that are in between, we have both large values of in-degree and out-degree. These are communicators. In this graph, there seems to be more talkers than listeners. Not surprising though. My social media friends show similar statistics.