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

Common network use cases

As I did in Chapter 1, Introducing Natural Language Processing, I will now also explain some of my own favorite use cases for working with network data. I mentioned at the beginning of the chapter that there are many different kinds of networks, but I personally prefer working with social networks and what I call dataflow networks.

Here are some of the uses I have for working with network data:

  • Mapping production dataflows
  • Mapping community interactions
  • Mapping literary social networks
  • Mapping historical social networks
  • Mapping language
  • Mapping dark networks

I will start with dataflow networks, as that was the first use case I realized for network data and something that revolutionized how I work.

Mapping production dataflow

As mentioned, this was the first idea that I had for my own use of network data. I have worked in software for over 20 years, and I have spent a significant amount of time “dissecting&...