Complex Network Analysis in Python: Recognize – Construct – Visualize – Analyze – Interpret
Complex Network Analysis in Python: Recognize – Construct – Visualize – Analyze – Interpret
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 you’re a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you’ll 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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Editorial Reviews
Review
- “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 understanding of the concepts without requiring Python programming experience.” – Kate Li, Ph.D., 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 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 healthcare, 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
About the Author
He has degrees in physics and computer science. His interests include computer simulation and modeling, network science, network analysis, and digital humanities. He teaches at Suffolk University in Boston, MA. He is an author. Data science is taught in Python. .
–This text refers to the paperback edition.
From the Publisher
From the Preface
Who should read this book
The book is intended for graduate and undergraduate students, as well as complex data analysis and social network analysis instructors. The book assumes that you have a background in computer programming. Common sense knowledge of complex networks is what it expects from you. The goal is to educate you about the elements of CNA and build up your programming skills. If you have been programming for a while, you can devote more attention to the techniques. If you?re a network analyst with less than an excellent background in Python programming, your plan should be to move slowly through the dark woods of data frames and list comprehensions and use your intuition to grasp programming concepts.
About the Book
The book covers construction, exploration, analysis, and visualization of complex networks using NetworkX, as well as several other Python modules, and Gephi, an interactive environment for network analysts. The book isn’t an introduction to Python. At the level of a freshman programming course, you already know the language. There are five parts to the book that cover specific aspects of complex networks. Each part has one or more detailed case studies.
The main Python CNA modules are NetworkX, iGraph, graph-tool, and networkit. After going over the construction of very simple networks using NetworkX and interactively, it concludes by presenting a network of Wikipedia pages related to complex networks.
You will look into networks based on explicit relationships in Part II. Network construction and measurement techniques are addressed in this part. A network of Panama papers illustrates money-laundering patterns in Central Asia.
Part III deals with networks based on spatial and temporal co-occurrences. The complex network structure is explored in the third part. It opens the way to network-based cultural domain analysis.
The contents of Part IV can help you build a network of items if you can’t find any direct or indirect relationships. If you learn how to find out if items are similar, you will be able to convert quantitative similarities into network edges. One of the outcomes of the fourth part is a network of psychological trauma types.
The book ends with Part V: directed networks with plenty of examples, including a network of qualitative adjectives that you could use in computer games or fiction.
You will be able to identify, sketch, transform, analyze, and visualize several types of networks when you finish your journey. You will be able to read network measures. The book is not intended to be a comprehensive reference. The whole story of network dynamics, as well as many discipline specific aspects, have been left unexplored. Economic networks, web scrapping, and classical social network analysis are just some of the destinations that the bibliography will take you to.
Additional information
| Best Sellers Rank | #451,599 in Kindle Store (See Top 100 in Kindle Store) #11 in Graph Theory (Kindle Store) #18 in LANs (Kindle Store) #34 in Software Utilities |
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