Table Of ContentEarly Praise for Complex Network Analysis in Python
This book is an excellent read for anyone who wants to learn the fundamentals
of complex network analysis with a focus on application. The case studies cover
a variety of topics and help readers link concepts to applications, providing readers
with a clear, well-structured, hands-on experience that deepens their understand-
ing of the concepts without requiring Python programming experience.
➤ Kate Li, PhD
Associate Professor, Sawyer Business School, Suffolk University
As a social scientist interested in network analysis but having limited knowledge
of Python, I found the book very useful. The author explains technical problems
in a way that is easy to understand for non–computer scientists. It is a great in-
troduction for those interested in network analysis seeking to apply the method
in their research.
➤ Weiqi Zhang
Associate Professor of Government, Suffolk University
Complex Network Analysis in Python is a thorough introduction to the tools and
techniques needed for complex network analysis. Real-world case studies
demonstrate how one can easily use powerful Python packages to analyze large
networks and derive meaningful analytic insights.
➤ Mike Lin
Senior Software Engineer, Fugue, Inc.
Having a deep understanding of complex network analysis is hard; however, this
book will help you learn the basics to start mastering the skills you need to analyze
complex networks, not only at a conceptual level, but also at a practical level, by
putting the theory into action using the Python programming language.
➤ Jose Arturo Mora
Head of Information Technology and Innovation, BNN Mexico
Complex networks have diverse applications in various fields, including health
care, social networks, and machine learning. I found this book to be an excellent
and comprehensive resource guide for researchers, students, and professionals
interested in applying complex networks.
➤ Rajesh Kumar Pandey
Graduate Student, IIT Hyderabad
Complex Network Analysis in Python
Recognize → Construct → Visualize → Analyze → Interpret
Dmitry Zinoviev
The Pragmatic Bookshelf
Raleigh, North Carolina
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ISBN-13: 978-1-68050-269-5
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Book version: P1.0—January 2018
To my beautiful and most intelligent wife,
Anna, and to our children: graceful ballerina,
Eugenia, and romantic gamer, Roman.
Contents
Acknowledgments . . . . . . . . . . . xi
Preface . . . . . . . . . . . . . . xiii
1. The Art of Seeing Networks . . . . . . . . . 1
Know Thy Networks 2
Enter Complex Network Analysis 5
Draw Your First Network with Paper and Pencil 6
Part I — Elementary Networks and Tools
2. Surveying the Tools of the Craft . . . . . . . . 11
Do Not Weave Your Own Networks 11
Glance at iGraph 12
Appreciate the Power of graph-tool 13
Accept NetworkX 15
Keep in Mind NetworKit 15
Compare the Toolkits 16
3. Introducing NetworkX . . . . . . . . . . 17
Construct a Simple Network with NetworkX 17
Add Attributes 23
Visualize a Network with Matplotlib 25
Share and Preserve Networks 29
4. Introducing Gephi . . . . . . . . . . . 31
Worth 1,000 Words 31
Import and Modify a Simple Network with Gephi 32
Explore the Network 34
Sketch the Network 36
Contents • viii
Prepare a Presentation-Quality Image 38
Combine Gephi and NetworkX 40
5. Case Study: Constructing a Network of Wikipedia Pages . . 41
Get the Data, Build the Network 42
Eliminate Duplicates 45
Truncate the Network 46
Explore the Network 47
Part II — Networks Based on Explicit Relationships
6. Understanding Social Networks . . . . . . . . 53
Understand Egocentric and Sociocentric Networks 53
Recognize Communication Networks 61
Appreciate Synthetic Networks 63
Distinguish Strong and Weak Ties 66
7. Mastering Advanced Network Construction . . . . . 69
Create Networks from Adjacency and Incidence Matrices 69
Work with Edge Lists and Node Dictionaries 76
Generate Synthetic Networks 78
Slice Weighted Networks 79
8. Measuring Networks . . . . . . . . . . . 83
Start with Global Measures 83
Explore Neighborhoods 84
Think in Terms of Paths 88
Choose the Right Centralities 92
Estimate Network Uniformity Through Assortativity 97
9. Case Study: Panama Papers . . . . . . . . 101
Create a Network of Entities and Officers 101
Draw the Network 104
Analyze the Network 105
Build a “Panama” Network with Pandas 108
Part III — Networks Based on Co-Occurrences
10. Constructing Semantic and Product Networks . . . . 115
Semantic Networks 116
Product Networks 120
