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

Other approaches to community detection

All of these algorithms that we have explored were ideas that people had on how to identify communities in networks, either based on nearness to other nodes or found by cutting edges. However, these are not the only approaches. I came up with an approach before learning about the Girvan-Newman algorithm that cut nodes rather than edges. However, when I learned about the Girvan-Newman approach, I found that to be more ideal and gave up on my implementation. But that makes me think, what other approaches might there be for identifying communities in networks?

As you learn more and become more comfortable working with networks, try to discover other ways of identifying communities.