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Network Science with Python and NetworkX Quick Start Guide

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Learn
  • Use Python and NetworkX to analyze the properties of individuals and relationships
  • Encode data in network nodes and edges using NetworkX
  • Manipulate, store, and summarize data in network nodes and edges
  • Visualize a network using circular, directed and shell layouts
  • Find out how simulating behavior on networks can give insights into real-world problems
  • Understand the ongoing impact of network science on society, and its ethical considerations
About

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.

Features
  • Understand the terminology and basic concepts of network science
  • Leverage the power of Python and NetworkX to represent data as a network
  • Apply common techniques for working with network data of varying sizes
Page Count 190
Course Length 5 hours 42 minutes
ISBN 9781789955316
Date Of Publication 25 Apr 2019
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
Centrality – finding key nodes
Bridges, brokers, and bottlenecks – betweenness centrality
Hubs – eigenvector centrality
Closeness centrality
Local clustering
Summary
References

Authors

Edward L. Platt

Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.