paid course

Network Analysis in R

In this course you'll learn to analyze and visualize network data with the igraph package.

  • 4 hours
  • 12 Videos
  • 50 Exercises
  • 4,061 Participants
  • 4,000 XP
James Curley
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.

See More
Collaborator(s)
  • Richie Cotton

    Richie Cotton

  • Nick Solomon

    Nick Solomon

Prerequisites

Course Description

In this course you'll learn how to work with and visualize network data. You'll use the igraph package to create networks from edgelists and adjacency matrices. You'll also learn how to plot networks and their attributes. Then, you'll learn how to identify important vertices using measures like betweenness and degree. Next, this course covers network structures, including triangles and cliques. Next, you'll learn how to identify special relationships between vertices, using metrics like assortativity. Finally, you'll see how to create interactive network plots using threejs.

In this course you'll learn to analyze and visualize network data with the igraph package.

Course Outline

  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.

  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.

Course Instructor

James Curley
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.

See More
Collaborator(s)
  • Richie Cotton

    Richie Cotton

  • Nick Solomon

    Nick Solomon

Join over 3,090,000 others learning to leverage the power of data with DataCamp!

Start Course For Free
Icon Icon Icon professional Icon info
Do you have 10 minutes to help us improve our service?
I'll do it No thanks