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

Graph embeddings in action

Now that we are past the comfort of community detection, we are getting into some weird territory with graph embeddings. The simplest way I think of graph embeddings is just the deconstruction of a complex network into a format more suitable for ML tasks. It’s the translation of a complex data structure into a less complex data structure. That’s a simple way of thinking about it.

Some unsupervised ML models will create more dimensions (more columns/features) of embeddings than others, as you will see in this section. In this section, we are going to create embeddings, inspect nodes that have similar embeddings, and then use the embeddings with supervised ML to predict “revolutionary or not,” like our “Spot the Revolutionary” game from the last chapter.

We’re going to quickly run through the use of several different models – this chapter would be hundreds of pages long if I went into great detail...