The last structural measure presented in this chapter is a little different from the ones seen so far. Betweenness, eigenvector, and closeness centrality all characterize a node by its relation to other nodes in the network. The measure presented in this section concerns the relationships between a node's neighbors, rather than those of the node itself. It is often useful to consider whether a node's neighbors tend to be connected to each other. In a social network, this question translates to asking whether the friend of a friend is also your friend, a property known as transitivity [to mathematicians who enjoy polysyllabic words]. The result of such relationships are triangles: three nodes, all mutually connected. The tendency for such triangles to arise is called clustering. When strong clustering is present, it often suggests robustness, and redundancy...
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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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