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

Product type Paperback

Published in Feb 2023

Publisher Packt

ISBN-13 9781801073691

Length 414 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

David Knickerbocker

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Table of Contents (17) Chapters

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

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Technical requirements

In this chapter, we will be using the NetworkX and pandas Python libraries. We will also import an NLTK tokenizer. By now, all of these libraries should be installed, so they should be ready for your use.

All the code for this chapter is available in this book’s GitHub repository at https://github.com/PacktPublishing/Network-Science-with-Python.

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

Table of Contents (17) Chapters

Technical requirements

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Authors (1)