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

Using embeddings in supervised ML

Alright! We’ve made it through some really fun hands-on work involving network construction, community detection, and both unsupervised and supervised ML; done some egocentric network visualization; and inspected the results of the use of different embeddings. This chapter really brought everything together. I hope you enjoyed the hands-on work as much as I did, and I hope you found it useful and informative. Before concluding this chapter, I want to go over the pros and cons of using embeddings the way that we have.

Please also keep in mind that there are many other classification models we could have tested with, not just Random Forest. You can use these embeddings in a neural network if you want, or you could test them with logistic regression. Use what you learned here and go have as much fun as possible while learning.

Pros and cons

Let’s discuss the pros and cons of using these embeddings. First, let’s start with...