A graph-based approach to modeling the spread of knowledge through social networks

Social media has completely revolutionized the data landscape. We’re more connected to every aside from now we have every been in human history. News stories can reach us instantly and concepts spread across the globe in days, but how does this work? How does information spread and may we model it? In this text, we are going to cover the idea behind information spread and use networks to model it.

In this text we are going to:

• Learn the fundamentals of graph and network theory
• Overview information diffusion and social contagion
• Construct a computational framework to simulate the spread of ideas

What are Graphs and Networks?

A graph is a mathematical structure that shows the relation between objects. It does this by representing each object as a vertex that’s connected to other vertices with edges that show the connection between them.

There are lots of varieties of graphs including weighted graphs where all of the sides are given values to not only show relation but strength of relation and directed graphs where the sides have a direction to them to point out directed relationships. The various varieties of graphs make them a robust tool to point out the connection between entities which we are going to discuss.

Conceptually, graphs and networks are an identical and normally the terms will be used interchangeably. Though, by convention, graph is used to check with the abstract mathematical concept and network is used for applied contexts. For example, we used the terms “computer network” and “social network” though computer or social graph would convey the identical thing. For our purposes we are going to say that a network is an applied graph and the one difference in terminology we’d like is referring to vertices as nodes as a substitute.