Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in color

Key Features

• Create networks using data points and information
• Learn to visualize and analyze networks to better understand communities
• Explore the use of network data in both - supervised and unsupervised machine learning projects

Book Description

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.

What you will learn

• Explore NLP, network science, and social network analysis
• Apply the tech stack used for NLP, network science, and analysis
• Extract insights from NLP and network data
• Generate personalized NLP and network projects
• Authenticate and scrape tweets, connections, the web, and data streams
• Discover the use of network data in machine learning projects

Who this book is for

Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both – social science and programming backgrounds will find a new perspective and add a feather to their hat.

Table of Contents

1. Introducing Natural Language Processing
2. Network Analysis
3. Useful Python Libraries
4. NLP and Network Synergy
5. Even Easier Scraping
6. Graph Construction and Cleaning
7. Whole Network Analysis
8. Egocentric Network Analysis
9. Community Detection
10. Supervised Machine Learning on Network Data
11. Unsupervised Machine Learning on Network Data

About the Author

David Knickerbocker is the chief engineer and co-founder of VAST-OSINT. He has over two decades of rich experience working with and around data in his career, with his focus being on data science, data engineering, software development, and cybersecurity.