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Simulation 106: Modeling Information Diffusion and Social Contagion with Networks | by Le Nguyen

Simulation 106: Modeling Information Diffusion and Social Contagion with Networks | by Le Nguyen


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

Social media has completely revolutionized the information landscape. We are more connected to each other than we have every been in human history. News stories can reach us in an instant and ideas spread across the globe in days, but how does this work? How does information spread and can we model it? In this article, we will cover the theory behind information spread and use networks to model it.

Figure 1: Artistic representation of our world connected by networks. Licensed under Creative Commons.

In this article we will:

  • Learn the basics of graph and network theory
  • Overview information diffusion and social contagion
  • Build 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 is connected to other vertices with edges that show the relationship between them.

Figure 2: Example of a graph with 7 edges and 6 vertices

There are many types of graphs including weighted graphs where all of the edges are given values to not just show relation but strength of relation and directed graphs where the edges have a direction to them to show directed relationships. The varying types of graphs make them a powerful tool to show the relationship between entities which we will discuss.

Conceptually, graphs and networks are identical and in most cases the terms can be used interchangeably. Though, by convention, graph is used to refer to the abstract mathematical concept and network is used for applied contexts. For instance, we used the terms “computer network” and “social network” even though computer or social graph would convey the same thing. For our purposes we will say that a network is an applied graph and the only difference in terminology we need is referring to vertices as nodes instead.



The article was first published here

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