Predictive Analytics using Networked Data in R
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Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Course Description
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.
What You’ll Learn
Introduction, networks and labelled networks
In this chapter you will be introduced to labelled networks, network learning and the challanges that can arise.
Network Featurization
In this chapter you will use the igraph package to compute various network features and add them to the network.
Homophily
In this chapter you will learn about homophily and how to compute the two measures that can be used to characterice it, dyadicity and heterophilicty.
Putting it all together
In this chapter you will use the network from Chapter 3 to create a flat dataset. Using standard data mining techniques, you will build predictive models and measure their performance with AUC and top decile lift.
