Summary
This chapter has been about a specific type of data – network data. We have learned that networks are used to represent relationships. Since relationships are prevalent in many real-world scenarios, it is essential that, as a data scientist, you are familiar with the main concepts and terminology relating to networks. Of these, the most important concepts we have covered in this chapter are as follows:
- What a network represents and that a network is a graph
- A graph,
, consists of a set of nodes (vertices), 
, and a set of edges, 
, between those nodes - The edges of a graph can be undirected or directed and can have weights associated with them
- The structure of a graph is encoded in the adjacency matrix
- The in-degree and out-degree of a node can be calculated from the adjacency matrix and tell us the number of edges coming into a node and leaving a node, respectively
- The sum of the node in-degree and out-degree values gives us the total degree...
