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

In this chapter, you learned a new kind of network analysis, called egocentric network analysis. I tend to call egocentric networks ego networks, to be concise. We’ve learned that we don’t have to analyze a network as a whole. We can analyze it in parts, allowing us to investigate a node's placement in the context of its relationship with another node.

Personally, egocentric network analysis is my favorite form of network analysis because I enjoy investigating the level of the individual things that exist in a network. Whole network analysis is useful as a broad map, but with egocentric network analysis, you can gain a really intimate understanding of the various relationships between things that exist in a network. I hope you enjoyed reading and learning from this chapter as much as I enjoyed writing it. I hope this inspires you to learn more.

In the next chapter, we will dive into community detection algorithms!