Network Science with Python and NetworkX Quick Start Guide电子书

Dedication

About Packt

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Contributors

About the author

About the reviewer

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Conventions used

Get in touch

Reviews

What is a Network?

Network science

The history of network science

Network science today

What is a network?

Nodes and edges

Visualizing networks

What is NetworkX?

Types of networks

Directed networks

Weighted networks

Understanding edges

Social networks

Flow networks

Similarity networks

Spatial networks

Your first network in NetworkX

Summary

References

Working with Networks in NetworkX

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

From Data to Networks

Modeling your data

Reading and writing network files

Creating a network with code

Summary

References

Affiliation Networks

Nodes and affiliations

Affiliation networks in NetworkX

Projections

Summary

References

The Small Scale - Nodes and Centrality

Centrality – finding key nodes

Bridges, brokers, and bottlenecks – betweenness centrality

Hubs – eigenvector centrality

Closeness centrality

Local clustering

Summary

References

The Big Picture - Describing Networks

The global structure of networks

Datasets

Diameter and mean shortest path

Global clustering

Measuring resilience

Minimum cuts

Connectivity

Centralization and inequality

Summary

References

In-Between - Communities

Communities – networks within networks

Community detection in NetworkX

Modularity maximization

Visualizing

An online social network

Girvan-Newman – betweenness-based communities

Cliques

K-cores

Summary

References

Social Networks and Going Viral

Social networks

Strong and weak ties

Tie strength

Bridge span

Comparing strength and span

The small world problem

Ring networks

A real social network

Random networks

Watts-Strogatz networks

Contagion – how things spread

Simple contagion

Complex contagion

Summary

References

Simulation and Analysis

Watts-Strogatz and small worlds

Preferential attachment and heavy-tailed networks

Configuration models

Agent-based models

Summary

References

Networks in Space and Time

Locations and events

Networks in space

Gravity models

Working with spatial data

Gravity model for air travel

Residual network

Network properties

Networks in time

Layered networks

Working with time data

The evolution of network properties

Summary

References

Visualization

Beyond the hairball

The circular layout

The shell layout

The force-directed layout

Null models

Summary

Conclusion

The practice of network science

Learning more

Advances in network science

The impact of network science

Appendix

Adjacency matrices

Biadjacency matrices

Modularity

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