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

Creating baseline WNA questions

I often jot down questions that I have before doing any kind of analysis. This sets the context of what I am looking for and sets up a framework for me to pursue those answers.

In doing any kind of WNA, I am interested in finding answers to each of these questions:

  • How big is the network?
  • How complex is the network?
  • What does the network visually look like?
  • What are the most important nodes in the network?
  • Are there islands, or just one big continent?
  • What communities can be found in the network?
  • What bridges exist in the network?
  • What do the layers of the network reveal?

These questions give me a start that I can use as a task list for running through network analysis. This allows me to have a disciplined approach when doing network analysis, and not just chase my own curiosity. Networks are noisy and chaotic, and this scaffolding gives me something to use to stay focused.

Revised SNA questions

In...