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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

Egocentric Network Analysis

The previous chapter was a whirlwind. We covered so much material, learning how to visualize and analyze whole networks. In comparison, this chapter should feel much simpler. It will also be much shorter. In previous chapters, we learned how to get and create network data, how to build graphs from network data, how to clean graph data, and how to do interesting things such as identifying communities. In this chapter, we will be doing what is called egocentric network analysis.

The good news is that everything that was learned in the previous chapter applies to egocentric networks. Centralities can be useful for finding important nodes. Community algorithms can be useful for identifying communities. The great news is that there really isn’t a lot that we need to cover in this chapter. Egocentric network analysis is simpler in scale as well as in scope. It’s most important that I explain how to get started, show what you can do, and explain...

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Tech Concepts
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Network Science with Python
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