Predictive Analytics using Networked Data in R

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

  1. 1

    Introduction, networks and labelled networks

    Free
    In this chapter you will be introduced to labelled networks, network learning and the challanges that can arise.
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  2. 2

    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.
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  3. 3

    Network Featurization

    In this chapter you will use the igraph package to compute various network features and add them to the network.
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  4. 4

    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.
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In the following tracks
Network Analysis
Collaborators
David CamposChester IsmayShon Inouye
Bart Baesens Headshot

Bart Baesens

Professor in Analytics and Data Science at KU Leuven
Bart Baesens is professor in Analytics and Data Science at the Faculty of Economics and Business of KU Leuven, and a lecturer at the University of Southampton (UK). He has done extensive research on big data & analytics, credit risk analytics and fraud analytics. He regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and fraud & credit risk management strategy.
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Maria Oskarsdottir Headshot

Maria Oskarsdottir

Post-doctoral Researcher
María Óskarsdóttir is a post-doctoral researcher and an active R user. She holds a PhD in Business Economics from KU Leuven (Belgium). Her research puts focus on applying social network analytics techniques for predictive modeling in marketing, credit scoring and insurance.
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