Paul Love has completed

# Network Analysis in R

4 hours
4,000 XP

## Get an Introduction to Networks

Discover the fundamental concepts in network analysis. This course begins by taking you through the basics of social networks, vertices and edges, and how you can use the igraph R package to explore and visualize network data.

You’ll move on to looking at directed networks in more detail, including the identification of key relationships between vertices and applying your new skills to a network data set looking at measles transmission in Hagelloch.

## Understand Network Structures and Graphs

Learn to characterize network structures and substructures by looking at network density and average path length. The third chapter of this course takes you through randomization and random graphs, before moving on to triangles, transitivity, and visualizing cliques.

## Identify Relationships Using Assortativity in igraph

Assortativity determines how likely two vertices are to be attached to each other if they share a common attribute - whether that’s numerical or categorical. You’ll explore the ASSORTATIVITY function within igraph to determine the impact of gender on a friendship network dataset, and will apply randomizations to assess your findings.

## Create Interactive Network Plots using threejs

At the end of this course, you’ll expand your knowledge beyond igraph to explore the network visualization capabilities of threejs. You’ll make your first interactive network plots using this R package, and will look at how you can further develop your visualization.

### .css-1goj2uy{margin-right:8px;}Group.css-gnv7tt{font-size:20px;font-weight:700;white-space:nowrap;}.css-12nwtlk{box-sizing:border-box;margin:0;min-width:0;color:#05192D;font-size:16px;line-height:1.5;font-size:20px;font-weight:700;white-space:nowrap;}Training 2 or more people?

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1. 1

### Introduction to networks

Free

In this chapter, you will be introduced to fundamental concepts in social network analysis. You will learn how to use the `igraph` R package to explore and analyze social network data as well as learning how to visualize networks.

Play Chapter Now
What are social networks?
50 xp
Creating an igraph object
100 xp
Counting vertices and edges
100 xp
Network attributes
50 xp
Node attributes and subsetting
100 xp
Edge attributes and subsetting
100 xp
Visualizing attributes
100 xp
Quiz on attributes
50 xp
Network visualization
50 xp
igraph network layouts
100 xp
Visualizing edges
100 xp
Quiz on igraph objects
50 xp
2. 2

### Identifying important vertices in a network

In this chapter you will learn about directed networks. You will also learn how to identify key relationships between vertices in a network as well as how to use these relationships to identify important or influential vertices. Throughout this chapter you will use a network of measles transmission. The data come from the German city of Hagelloch in 1861. Each directed edge of the network indicates a child becoming infected with measles after coming into contact with an infected child.

3. 3

### Characterizing network structures

This module will show how to characterize global network structures and sub-structures. It will also introduce generating random network graphs.

4. 4

### Identifying special relationships

This chapter will further explore the partitioning of networks into sub-networks and determining which vertices are more highly related to one another than others. You will also develop visualization methods by creating three-dimensional visualizations.

In the following tracks

Network Analysis

Collaborators

Prerequisites

Intermediate R
JAMES CURLEY

Associate Professor at UT Austin

James P. Curley is an Associate Professor in the Department of Psychology at University of Texas Austin in the Behavioral Neuroscience division. He received a Ph.D. from Cambridge University in 2003 and completed post-doctoral research at Cambridge University and Columbia University before becoming an Assistant Professor of Psychology at Columbia University in 2012. Dr. Curley’s current research interests are in studying the social dynamics of animals. Dr. Curley teaches several undergraduate and graduate courses, including R programming for Behavioral Scientists and Statistics & Research Design.
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