Dmitry Zinoviev - Complex Network Analysis in Python
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Abstract: Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If youre a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, youll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems
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To my beautiful and most intelligent wife, Anna, and to our children: graceful ballerina, Eugenia, and romantic gamer, Roman.
This book is an excellent read for anyone who wants to learn the fundamentals of complex network analysis with a focus onapplication. The case studies cover avariety of topics and help readers link concepts to applications, providing readers with a clear, well-structured, hands-on experience that deepens their understanding 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 introduction 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 casestudies 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 startmastering 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, andprofessionals interested in applying complex networks.
| Rajesh Kumar Pandey |
| Graduate Student, IIT Hyderabad |
Acknowledgments
This book would not be possible without my editor, Adaobi Obi Tulton. She had the courage to learn the dark inner secrets of complex network analysis and guided me through the minefields of manuscript preparation, from the fuzzy ideas at the onset to this very book in flesh and blood. Thank you, Adaobi.
I am grateful to my reviewers (in alphabetical order):Cody Buntain (University of Maryland),Remy Cazabet (Lyon University),Mark Chu-Carroll (Imagen Technologies),Raphal Fournier-Sniehotta (CDRIC),Michael Lin (Fugue Inc.),Jason Montojo (University of Toronto),Jose Arturo Mora (EY, BNN Mexico),Prasham Ojha (University of Koblenz-Landau), Talha Oz (George Mason University), andRajesh Kumar Pandey (Gade Autonomous Systems). Your reviews were indispensable. They profoundly affected the books style, structure, and usability. Thank you, my reviewers.
My wife, Anna; my children, Eugenia and Roman; and my friends and colleagues from Suffolk University provided much-needed emotional support. Writing a book is a quest. It feels good to be well supported. Thank you, my supporters.
Last but not least, the early readers of the beta book provided the errata requests. Errare humanum est , but the book is better without errors. Thank you, my early readers.
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