Dmitry Zinoviev @aqsaqal
Edited by Adaobi Obi Tulton @aotulton
Construct, analyze, and visualize networks with networkx, a Python language module. Discover how to work with 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.
Complex network analysis used to be done by hand or with non-programmable network analysis tools, but you can now automate and program these tasks in Python. Convert real-life and synthetic network graphs into networkx data structures. Handle centrality calculation, blockmodeling, and clique and community detection. Masater network visualization tools. Explore big networks with NetworKit, a high-performance networkx substitute. Learn from experts in 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.
“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 to use powerful Python packages to analyze large networks and derive meaningful analytic insights.”
–Mike Lin, Senior Software Engineer, Fugue, Inc.
- Full details: Complex Network Analysis in Python: Recognize → Construct → Visualize → Analyze → Interpret by Dmitry Zinoviev
- View this book’s portal and details on how to post errata and suggestions here.
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