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Network Science with Python
Network Science with Python

Network Science with Python: Explore the networks around us using network science, social network analysis, and machine learning

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Network Science with Python

Introducing Natural Language Processing

Why in the world would a network analysis book start with Natural Language Processing (NLP)?! I expect you to be asking yourself that question, and it’s a very good question. Here is why: we humans use language and text to describe the world around us. We write about the people we know, the things we do, the places we visit, and so on. Text can be used to reveal relationships that exist. The relationship between things can be shown via network visualization. It can be studied with network analysis.

In short, text can be used to extract interesting relationships, and networks can be used to study those relationships much further. We will use text and NLP to identify relationships and network analysis and visualization to learn more.

NLP is very useful for creating network data, and we can use that network data to learn network analysis. This book is an opportunity to learn a bit about NLP and network analysis, and how they can be used together.

In explaining NLP at a very high level, we will be discussing the following topics:

  • What is NLP?
  • Why NLP in a network analysis book?
  • A very brief history of NLP
  • How has NLP helped me?
  • Common uses for NLP
  • Advanced uses of NLP
  • How can a beginner get started with NLP?

Technical requirements

Although there are a few places in this chapter where I show some code, I do not expect you to write the code yet. These examples are only for a demonstration to give a preview of what can be done. The rest of this book will be very hands-on, so take a look and read to understand what I am doing. Don’t worry about writing code yet. First, learn the concepts.

What is NLP?

NLP is a set of techniques that helps computers work with human language. However, it can be used for more than dealing with words and sentences. It can also work with application log files, source code, or anything else where human text is used, and on imaginary languages as well, so long as the text is consistent in following a language’s rules. Natural language is a language that humans speak or write. Processing is the act of a computer using data. So, NLP is the act of a computer using spoken or written human language. It’s that simple.

Many of us software developers have been doing NLP for years, maybe even without realizing it. I will give my own example. I started my career as a web developer. I was entirely self-educated in web development. Early in my career, I built a website that became very popular and had a nice community, so I took inspiration from Yahoo Chats (popular at the time), reverse-engineered it, and built my own internet message board. It grew rapidly, providing years of entertainment and making me some close friends. However, with any good social application, trolls, bots, and generally nasty people eventually became a problem, so I needed a way to flag and quarantine abusive content automatically.

Back then, I created lists of examples of abusive words and strings that could help catch abuse. I was not interested in stopping all obscenities, as I do not believe in completely controlling how people post text online; however, I was looking to identify toxic behavior, violence, and other nasty things. Anyone with a comment section on their website is very likely doing something similar in order to moderate their website, or they should be. The point is that I have been doing NLP since the beginning of my career without even noticing, but it was rule-based.

These days, machine learning dominates the NLP landscape, as we are able to train models to detect abuse, violence, or pretty much anything we can imagine, which is one thing that I love the most about NLP. I feel that I am limited only by the extent of my own creativity. As such, I have created classifiers to detect discussions that contained or were about extreme political sentiment, violence, music, art, data science, natural sciences, and disinformation, and at any given moment, I typically have several NLP models in mind that I want to build but haven’t found time. I have even used NLP to detect malware. But, again, NLP doesn’t have to be against written or spoken words, as my malware classifier has shown. If you keep that in mind, then your potential uses for NLP massively expand. My rule of thumb is that if there are sequences in data that can be extracted as words – even if they are not words – they can potentially be used with NLP techniques.

In the past, and probably still now, analysts would drop columns containing text or do very basic transformations or computations, such as one-hot encoding, counts, or determining the presence/absence (true/false). However, there is so much more that you can do, and I hope this chapter and book will ignite some inspiration and curiosity in you from reading this.

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Key benefits

  • Create networks using data points and information
  • Learn to visualize and analyze networks to better understand communities
  • Explore the use of network data in both - supervised and unsupervised machine learning projects
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You’ll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You’ll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you’ll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You’ll also explore network analysis concepts, from basics to an advanced level. By the end of the book, you’ll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.

Who is this book for?

Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both – social science and programming backgrounds will find a new perspective and add a feather to their hat.

What you will learn

  • Explore NLP, network science, and social network analysis
  • Apply the tech stack used for NLP, network science, and analysis
  • Extract insights from NLP and network data
  • Generate personalized NLP and network projects
  • Authenticate and scrape tweets, connections, the web, and data streams
  • Discover the use of network data in machine learning projects
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Publication date : Feb 28, 2023
Length: 414 pages
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Language : English
ISBN-13 : 9781801073691
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Length: 414 pages
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Table of Contents

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

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Kindle Customer Dec 04, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I learned all about graphs and networks in College a few decades ago. It has proven fairly useless knowledge to me.This book, on the other hand, is incredible. I can now see how these tools and approaches are relevant to my work in data. It's really reshaped how I think of data problems through brilliant examples and with appropriate context.David has taken the Academic and made it Useful. And it's wicked cool!
Amazon Verified review Amazon
Om S Mar 12, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book "Network Science with Python" is an excellent resource for anyone looking to learn how to create networks using data points and information. The author provides a hands-on approach to learning about network science, natural language processing, and social network analysis.The book is well-organized, with a clear and concise table of contents that makes it easy to navigate. The first few chapters introduce the basics of natural language processing and network science, followed by useful Python libraries for these topics. The author then demonstrates how to extract insights from NLP and network data, and generate personalized NLP and network projects.One of the standout features of this book is its emphasis on practical applications of network analysis. The author includes detailed instructions on how to authenticate and scrape tweets, connections, the web, and data streams. Readers will learn how to construct and clean networks, conduct egocentric network analysis, and detect communities within networks.The final chapters of the book explore the use of network data in both supervised and unsupervised machine learning projects. The author provides detailed explanations of these concepts, making them accessible to readers from both social science and programming backgrounds.Overall, "Network Science with Python" is a valuable resource for anyone interested in learning about network analysis and its practical applications. The inclusion of specific technical and mathematical details ensures that readers will have a solid foundation for future reference. The purchase of the print or Kindle book also includes a free PDF eBook, making it a great value for anyone looking to expand their knowledge of network science.
Amazon Verified review Amazon
zuluwhiskey May 17, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a data scientist, NLP is an area I've had limited experience in. It was never as striking to me as other areas of data science. But I've followed David Knickerbocker for a while now and was happy to purchase this the moment I saw his announcement on LinkedIn. It is hard for me to imagine a better introduction to this topic and it was, I think, precisely what I needed. The linear progression and friendly tone makes it highly bingeable. It is written with contagious enthusiasm and seasoned wisdom. It quite plainly communicates the profundity and power of a good network analysis - and this is what I never understood, why I should ever care, what all can really be done with a graph? David answers by providing quickly digestible examples and inspires a curiosity to explore. This is a hit-the-ground-running book, a why-should-I-care-answer book, and a I-didn't-know-I'd-find-this-so-interesting book. Needless to say, I look forward to exploring these concepts with my own projects. David's enthusiasm is indeed contagious.
Amazon Verified review Amazon
Ben Bearden Mar 21, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've had an awesome time reading and applying the concepts of David Knickerbocker's book "Network Science with Python" to my own research.Above is a social network of Russian propaganda channels on Telegram that I've made using the techniques he laid out in his book.It is still a work in progress, but I am baffled by the applicability of network science to OSINT and research in general.Still reading but am thoroughly enjoying it thus far!
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Gerald H. SHUDY Jr. May 10, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Having followed David on LinkedIn for some time and getting advice from him occasionally, I was very excited to get the “collected wisdom” of his journey in an easy-to follow and FUN book!It’s everything I hoped for and even though I’m not finished yet, I had to come here to say this. :-)
Amazon Verified review Amazon
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