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Networks appear in lots of places in today's world. From social media to organizational charts to transportation, they can be visualized and analyzed using R. This track shows you how.

Learn how to visualize and analyze network data in R, using igraph and other visualization libraries.

with R

Network Analysis

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.

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

Network Analysis in R

In this course, you will learn to perform state-of-the art predictive analytics using networked data in R.  The aim of network analytics is to predict to which class a network node belongs, such as churner or not, fraudster or not, defaulter or not, etc.  To accomplish this, we discuss how to leverage information from the network and its underlying structure in a predictive way.  More specifically, we introduce the idea of featurization such that network features can be added to non-network features as such boosting the performance of any resulting analytical model.  In this course, you will use the igraph package to generate and label a network of customers in a churn setting and learn about the foundations of network learning. Then, you will learn about homophily, dyadicity and heterophilicty, and how these can be used to get key exploratory insights in your network. Next, you will use the functionality of the igraph package to compute various network features to calculate both node-centric as well as neighbor based network features.  Furthermore, you will use the Google PageRank algorithm to compute network features and empirically validate their predictive power. Finally, we teach you how to generate a flat dataset from the network and analyze it using logistic regression and random forests.

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

Predictive Analytics using Networked Data in R

Now that you're familiar with the basics of network analysis it's time to see how to apply those concepts to large real-world data sets. You'll work through three different case studies, each building on your previous work. These case studies are working with the kinds of data you'll see in both academic and industry settings. We'll explore some of the computational and visualization challenges you'll face and how to overcome them. Your knowledge of igraph will continue to grow, but we'll also leverage other visualization libraries that will help you bring your visualizations to the web.

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Network Analysis with R

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Loved by learners at thousands of companies

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Join over .css-1osvizq-TrackShowPage{color:#03ef62;}12,310,000 learners and start Network Analysis with R today!

Create Your Free Account

Join over 12,310,000 learners and start Network Analysis with R today!