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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem 2. Data Acquisition FREE CHAPTER 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

GDELT dataset


In order to validate our implementation, we use the GDELT dataset we analyzed in the previous chapter. We extracted all of the communities and spent some time looking at the person names to see whether or not our community clustering was consistent. The full picture of the communities is reported in Figure 7 and has been realized using the Gephi software, where only the top few thousand connections have been imported:

Figure 7: Community detection on January 12

We first observe that most of the communities we detected are totally aligned with the ones we could eyeball on a force-directed layout, giving a good confidence level about the algorithm accuracy.

The Bowie effect

Any well-defined community has been properly identified, and the less obvious ones are the ones surrounding highly connected vertices such as David Bowie. The name David Bowie being heavily mentioned in GDELT articles alongside so many different persons that, on that day of January 12, 2016, it became too large...

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