Network Science with Python and NetworkX Quick Start Guide: Explore and visualize network data effectively

Network Science with Python and NetworkX Quick Start Guide: Explore and visualize network data effectively
Network Science with Python and NetworkX Quick Start Guide: Explore and visualize network data effectively by Edward L. Platt
English | 2019 | ISBN: 1789955316 | 190 Pages | EPUB | 27 MB

Manipulate and analyze network data with the power of Python and NetworkX
NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use.
If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts.
By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.
What you will learn

  • Use Python and NetworkX to analyze the properties of individuals and relationships
  • Encode data in network nodes and edges using NetworkX
  • Manipulate, store, and summarize data in network nodes and edges
  • Visualize a network using circular, directed and shell layouts
  • Find out how simulating behavior on networks can give insights into real-world problems
  • Understand the ongoing impact of network science on society, and its ethical considerations