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Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide

By : Platt
5 (3)
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Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide

5 (3)
By: Platt

Overview of this book

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.
Table of Contents (15 chapters)
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Social Networks and Going Viral

Network analysis is often used to understand the behavior of groups of people. Relationships within a group of people form a kind of network—a social network. Social networks are some of the longest-studied in network science, and provide some of the results most directly applicable to everyday life. This chapter will introduce you to the elementary results in social network analysis.

Topics in this chapter include the following:

  • Social networks: The history of social networks in network science
  • Strong and weak ties: How to interpret and quantify the intensity of relationships
  • The small world problem: Understanding how very large networks can be spanned by relatively short paths
  • Contagion: How information, diseases, and anything else spreads over networks

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