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Network Science with Python

Network Science with Python

By : David Knickerbocker
5 (15)
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Network Science with Python

Network Science with Python

5 (15)
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)
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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

Whole Network Analysis

In previous chapters, we spent a lot of time covering how networks can be constructed using text and how cleanup can be done on networks. In this chapter, we are moving on to whole network analysis. For the sake of simplicity, I will call it WNA. WNA is done to get the lay of the land, to understand the denseness of a network, which nodes are most important in various ways, which communities exist, and so forth. I’m going to cover material that I have found useful, which is a bit different from what is found in most social network analysis (SNA) or network science books. I do applied network science every day, and my goal is to showcase some of the options that are available to allow readers to very quickly get started in network analysis.

Network science and SNA are both very rich topics, and if you find any section of this chapter especially interesting, I encourage you to do your own research to learn more. Throughout this book, I will reference...

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Network Science with Python
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