Book Image

Network Science with Python

By : David Knickerbocker
Book Image

Network Science with Python

By: David Knickerbocker

Overview of this book

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.
Table of Contents (17 chapters)
1
Part 1: Getting Started with Natural Language Processing and Networks
5
Part 2: Graph Construction and Cleanup
9
Part 3: Network Science and Social Network Analysis

Summary

I can’t believe we’ve made it to this point. At the beginning of this book, this felt like an impossible task, and yet here we are. In order to do the hands-on exercises for this chapter, we’ve used what we learned in the previous chapters. I hope I have shown you how networks can be useful, and how to work with them.

At the beginning of this book, I set out to write a practical hands-on book that would be code-heavy, not math-heavy. There are tons of network analysis books out there that have an emphasis on math but do not show actual implementation very well, or at all. I hope this book has effectively bridged the gap, giving a new skill to coders, and showing social scientists programmatic ways to take their network analysis to new heights. Thank you so much for reading this book!