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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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Toc

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

Community detection algorithm


Community detection has become a popular field of research over the past few decades. Sadly, it did not move as fast as the digital world that a true data scientist lives in, with more and more data collected every second. As a result, most of the proposed solutions are simply not suitable for a big data environment.

Although a lot of algorithms suggest a new scalable way for detecting communities, none of them is actually meaning scalable in a sense of distributed algorithms and parallel computing.

Louvain algorithm

Louvain algorithm is probably the most popular and widely used algorithm for detecting communities on undirected weighted graphs.

Note

For more information about Louvain algorithm, refer to the publication: Fast unfolding of communities in large networks. Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre. 2008

The idea is to start with each vertex being the center of its own community. At each step, we look for community neighbors...

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