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Network Science with Python

You're reading from   Network Science with Python Explore the networks around us using network science, social network analysis, and machine learning

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781801073691
Length 414 pages
Edition 1st Edition
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Author (1):
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David Knickerbocker David Knickerbocker
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David Knickerbocker
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Natural Language Processing and Networks
2. Chapter 1: Introducing Natural Language Processing FREE CHAPTER 3. Chapter 2: Network Analysis 4. Chapter 3: Useful Python Libraries 5. Part 2: Graph Construction and Cleanup
6. Chapter 4: NLP and Network Synergy 7. Chapter 5: Even Easier Scraping! 8. Chapter 6: Graph Construction and Cleaning 9. Part 3: Network Science and Social Network Analysis
10. Chapter 7: Whole Network Analysis 11. Chapter 8: Egocentric Network Analysis 12. Chapter 9: Community Detection 13. Chapter 10: Supervised Machine Learning on Network Data 14. Chapter 11: Unsupervised Machine Learning on Network Data 15. Index 16. Other Books You May Enjoy

Using the Louvain method

The Louvain method is certainly my favorite for community detection, for a few reasons.

First, this algorithm can be used on very large networks of millions of nodes and it will be effective and fast. Other approaches that we will explore in this chapter will not work on large networks and will not be as fast, so we get effectiveness and speed with this algorithm that we can’t find anywhere else. As such, it is my go-to algorithm for community detection, and I save the others as options to consider.

Second, it is possible to tune the resolution parameter to find the best partitions for community detection, giving flexibility when the default results are not optimal. With the other algorithms, you do not have this flexibility.

In summary, with the Louvain method, we have a fast algorithm that is effective at community detection in massive networks, and we can optimize the algorithm for better results. I recommend dabbling in community detection...

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