When the TCP/IP suite of telecommunication protocols was introduced in 1974, it suddenly became possible for computer networks using different hardware and software to communicate with one another—to internetwork—creating what we now know as the internet. On a related note, many of the approximately 13,000 proteins found in fruit flies interact with at least one other protein. Among these, smaller groups can be found in which each protein interacts with most of the others. It may not be immediately obvious what fruit flies have to do with the internet (aside from both being found on apples). The connection is this: as with many different kinds of networks, both the internet and protein interaction networks have smaller sub-networks with both internal structure and external relationships. In network science, these groups...
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Network Science with Python and NetworkX Quick Start Guide
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Network Science with Python and NetworkX Quick Start Guide
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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)
Preface
Free Chapter
What is a Network?
Working with Networks in NetworkX
From Data to Networks
Affiliation Networks
The Small Scale - Nodes and Centrality
The Big Picture - Describing Networks
In-Between - Communities
Social Networks and Going Viral
Simulation and Analysis
Networks in Space and Time
Visualization
Conclusion
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