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Network Analysis in R

Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

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4 Hours12 Videos50 Exercises16,503 Learners4000 XPNetwork Analysis Track

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Course Description

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.
  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.

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    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.

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  3. 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

Collaborators

richie
Richie Cotton
nicksolomon
Nick Solomon

Prerequisites

Intermediate R
JAMES CURLEY Headshot

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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