Course
Network Analysis in R
IntermediateSkill Level
Updated 01/2023
RProbability & Statistics4 hr12 videos50 Exercises4,000 XP21,151Statement of Accomplishment
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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.Prerequisites
Intermediate R1
Introduction to networks
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. 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
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
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.
Network Analysis in R
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FAQs
Is this course suitable for beginners?
This course is not suitable for complete beginners to R. We recommend that you either take, or understand the concepts in, our Intermediate R course in order to make the most of this course. If you would like to develop your network analysis skills further, this course is a part of our Network Analysis track which offers a pathway to more in-depth learning.
What is igraph?
igraph is a package available in Python and R that consists of a number of network analysis tools. It is open source, and easily portable between systems. It is considerably faster than many other competitors such as networkx.
Is R good for network analysis?
R is a statistical language which is well-suited to network analysis. It also has a range of packages, such as igraph, that have been created to perform this type of analysis and manipulate network data.
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