Network Analysis in R

In this course you'll learn to analyze and visualize network data with the igraph package.
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4 Hours12 Videos50 Exercises12,238 Learners
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Course Description

In this course you'll learn how to work with and visualize network data. You'll use the <code>igraph</code> 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.

  1. 1

    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.
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  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.
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  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.
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  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.
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In the following tracks
Network Analysis
Richie CottonNick Solomon
Intermediate R


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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