Book Image

Network Science with Python and NetworkX Quick Start Guide

By : Edward L. Platt
Book Image

Network Science with Python and NetworkX Quick Start Guide

By: Edward L. 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.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
What is a Network?
What is a Network?
Network science
What is a network?
What is NetworkX?
Types of networks
Your first network in NetworkX
Summary
References
2
Working with Networks in NetworkX
Working with Networks in NetworkX
The Graph class – undirected networks
Adding attributes to nodes and edges
Adding edge weights
The DiGraph class – when direction matters
MultiGraph and MultiDiGraph – parallel edges
Summary
References
3
From Data to Networks
From Data to Networks
Modeling your data
Reading and writing network files
Creating a network with code
Summary
References
4
Affiliation Networks
Affiliation Networks
Nodes and affiliations
Affiliation networks in NetworkX
Projections
Summary
References
5
The Small Scale - Nodes and Centrality
The Small Scale - Nodes and Centrality
Centrality – finding key nodes
Bridges, brokers, and bottlenecks – betweenness centrality
Hubs – eigenvector centrality
Closeness centrality
Local clustering
Summary
References
6
The Big Picture - Describing Networks
The Big Picture - Describing Networks
The global structure of networks
Diameter and mean shortest path
Global clustering
Measuring resilience
Centralization and inequality
Summary
References
7
In-Between - Communities
In-Between - Communities
Communities – networks within networks
Community detection in NetworkX
Cliques
K-cores
Summary
References
8
Social Networks and Going Viral
Social Networks and Going Viral
Social networks
Strong and weak ties
The small world problem
Contagion – how things spread
Summary
References
9
Simulation and Analysis
Simulation and Analysis
Watts-Strogatz and small worlds
Preferential attachment and heavy-tailed networks
Configuration models
Agent-based models
Summary
References
10
Networks in Space and Time
Networks in Space and Time
Locations and events
Networks in space
Networks in time
Summary
References
11
Visualization
Visualization
Beyond the hairball
The circular layout
The shell layout
The force-directed layout
Summary
12
Conclusion
Conclusion
The practice of network science
Learning more
Advances in network science
The impact of network science
13
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Appendix
Appendix
Adjacency matrices
Biadjacency matrices
Modularity

Working with Networks in NetworkX

The basic features of NetworkX are contained in several Python classes that represent different types of networks. In particular, this chapter discusses Graph, DiGraph, MultiGraph, and MultiDiGraph. These classes can be used to represent, analyze, and visualize most networks. In this chapter, you will learn to use these classes to work with real-world network data in NetworkX. The code examples in this and future chapters will assume that you have already imported the networkx package.

In this chapter, we will cover the following topics:

  • The Graph class: Understand the properties of undirected networks and how they are represented using the NetworkX Graph class.
  • Attributes: How to associate data with nodes and edges.
  • Edge weights: Learn how to quantify connection strength, and annotate edges with that information.
  • The DiGraph class: Understand...
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