This chapter has shown how to analyze the microscale structure of networks by calculating centrality measures and other node-based measures of network structure. Betweenness centrality identifies bridges and brokers: edges and nodes that connect otherwise poorly connected parts of a network. Eigenvector centrality identifies nodes that are connected to other well-connected nodes. Closeness centrality identifies nodes that are, on average, closest to other nodes. Finally, the triangle count and local clustering coefficient quantify how well-connected a node's friends are. By examining a historical social network of suffragette activists, we saw that ranking highly on one centrality value doesn't necessarily mean a node ranks highly on others. While sometimes correlated, different centrality values measure different things, so meaningful results require choosing...
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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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