# Network Analysis in the Tidyverse

Learn how to analyze and visualize network data in the R programming language using the tidyverse approach.

4 Hours12 Videos47 Exercises5,327 Learners3950 XPNetwork Analysis Track

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

## Course Description

If you've ever wanted to understand more about social networks, information networks, or even the neural networks of our brains, then you need to know network science! It will demonstrate network analysis using several R packages, including dplyr, ggplot2, igraph, ggraph as well as visNetwork. You will take on the role of Interpol Analyst and investigate the terrorist network behind the Madrid train bombing in 2004. Following the course, you will be able to analyse any network with basic centrality and similarity measures and create beautiful and interactive network visualizations.

1. 1

### The hubs of the network

Free

The challenge in this chapter is to spot the most highly connected terrorists in the network. We will first import the dataset and build the network. Then we will learn how to visualize it in different layouts using ggraph package. Later on, we will compute two basic yet important centrality measures in network science - degree and strength. We will use them to spot highly connected terrorists. We will finally touch two alternative centrality measures, betweenness and closeness.

Network Science
50 xp
Explore the dataset
100 xp
Build and explore the network (part 1)
100 xp
Build and explore the network (part 2)
100 xp
Visualizing networks
50 xp
Visualize the network (part 1)
100 xp
Visualize the network (part 2)
100 xp
Centrality measures
50 xp
Find the most connected terrorists
100 xp
Find the most strongly connected terrorists
100 xp
More on centrality
50 xp
2. 2

### In its weakness lies its strength

In this chapter we will spot the most influential ties among terrorists in the network. We will use a centrality measure on ties, called betweenness, and will learn how to visualize the network highlighting connections with high betweenness centrality. Moreover, we will provide some alternative evidence regarding Mark Granovetter's theory of strength of weak ties, confirming that looser connections are crucial as demonstrated in the Madrid terrorism network.

3. 3

### Connection patterns

The challenge in this chapter is to discover pairs of similar (and dissimilar) terrorists. We will introduce the adjacency matrix as a mathematical representation of a network and use it to find terrorists with similar connection patterns. We will also learn how to visualize similar and dissimilar pairs of individuals using ggraph.

4. 4

### Similarity clusters

In this chapter we will discover cells of similar terrorists. We will explore hierarchical clustering to find groups of similar terrorists building on the notion of similarity of connection patterns developed in the previous chapter. Furthermore, we will explore the visNetwork package to produce fulfilling interactive network visualizations.

In the following tracks

Network Analysis

Collaborators

Chester IsmayBecca Robins

#### Massimo Franceschet

Professor of Data Science at the University of Udine (Italy)

I am currently interested in data science, complex networks, bibliometrics and generative art. A long time ago I was fond of logic and even before of artificial intelligence. Outside academia, I approached theater and contemporary dance. I love to demystify complex ideas and to cross-fertilize science and art.

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden