Welcome to the first post of Graphs for Data Science!
In this first post we will introduce some fundamental concepts of Network Science (or Graph Theory, depending on how you roll) using a real world data set.
Graphs (if you’re coming from Math or CS) or Networks (if you’re a Physicist or Engineer) are pervasive in our daily lives.
At their simplest, Graphs are just a set of objects and their interconnections. Depending on the specific dataset we are dealing with, each object can be just about anything we care about: a person, a city, an animal, a concept, a movie, a webpage, a word, a component, etc. We generally refer to these as “Nodes” or “Vertices”.
Connections between nodes are just as arbitrary and are simply a way to represent a relationship between two similar objects. Connections are commonly referred to as “Edges”, “Links” or “Arcs” .
From these two simple definitions it’s easy to see how common place graphs are. Indeed, the pages you visit on the web, the roads you navigate …
