Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781801073691
Length 414 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
David Knickerbocker David Knickerbocker
Author Profile Icon David Knickerbocker
David Knickerbocker
Arrow right icon
View More author details
Toc

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

Other use cases

While this may be interesting, most of us don’t hunt revolutionaries as part of our day jobs. So, what good is this? Well, there are lots of uses for doing predictions against networks. Lately, graph ML has gotten a lot of interest, but most articles and books tend to showcase models built by other people (not how to build them from scratch), or use NNs. This is fine, but it’s complicated, and not always practical.

This approach that I showed is lightweight and practical. If you have network data, you could do something similar.

But what other use cases are there? For me, the ones I’m most interested in are bot detection, and the detection of artificial amplification. How would we do that? For bot detection, you may want to look at features such as the age of the account in days, the number of posts made over time (real people tend to slowly learn how to use a social network before becoming active), and so on. For artificial amplification...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image