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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Concepts
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Creating a graph


At this point in our experiment, we have a list of users and their friends. This gives us a graph where some users are friends of other users (although not necessarily the other way around).

A graph is a set of nodes and edges. Nodes are usually objects of interest - in this case, they are our users. The edges in this initial graph indicate that user A is a friend of user B. We call this a directed graph, as the order of the nodes matters. Just because user A is a friend of user B, that doesn't imply that user B is a friend of user A. The example network below shows this, along with a user C who is friends of user B, and is friended in turn by user B as well:

In python, one of the best libraries for working with graphs, including creating, visualising and computing, is called NetworkX.

Note

Once again, you can use Anaconda to install NetworkX: conda install networkx

First, we create a directed graph using NetworkX. By convention, when importing NetworkX, we use the abbreviation...

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