This chapter introduced affiliation networks and the tools provided by NetworkX for working with affiliation networks, with special attention to using projections to create co-affiliation networks. Affiliation networks are ubiquitous in network data. Whenever there is a symmetrical relationship that can connect more than two things, there is an underlying affiliation structure. Many single-mode networks are really co-affiliation networks—projections of affiliation networks onto one type of node. Different projections have different interpretations, such as the number of paths or similarities. Choosing an appropriate projection for the data and for the question being asked can reveal important properties of a network that might otherwise be overlooked. Even as you encounter standard single-mode networks in the rest of this book, and in real-world data, it is often...
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.
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Network Science with Python and NetworkX Quick Start Guide
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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