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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
Author Profile Icon David Knickerbocker
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

Visualizing subgraphs

Often, in network analysis, we will want to see just a portion of the network, and how nodes in that portion link to each other. For instance, if I have a list of 100 web domains of interest or social media accounts, then it may be useful to create a subgraph of the whole graph for analysis and visualization.

For the analysis of a subgraph, everything in this chapter is still applicable. You can use centralities on subgraphs to identify important nodes in a community, for instance. You can also use community detection algorithms to identify communities that exist in a subgraph when the communities are unknown.

Visualizing subgraphs is also useful when you want to remove most of the noise in a network and investigate how certain nodes interact. Visualizing a subgraph is identical to how we visualize whole networks, ego graphs, and temporal graphs. But creating subgraphs takes a tiny bit of work. First, we need to identify the nodes of interest, then we need...

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