Complex network analysis of collections of connected items, words, concepts, or people can now be automated and programmed in Python. Author Dmitry Zinoviev shows how to construct, analyze, and visualize networks with networkx, a Python language module, to explore their structure and individual elements. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer.
Author: Dmitry Zinoviev
Publisher: Pragmatic Bookshelf
Date: January 2018
Audience: Python programmers interested in network analysis, or complex data analysis (CNA) or social network analysis (SNA) instructors, researchers and practitioners.
Category: Data Science
- Explore simple networks
- Convert real-life and synthetic network graphs into networkx data structures.
- Learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection.
- Get familiar with presentation-quality network visualization tools such as Gephi
- Adapt the patterns from the case studies to your problems.
- Explore big networks with NetworKit, a high-performance networkx substitute.
For recommendations of Data Analysis books see Reading Your Way Into Big Data, and for books on Python, see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.
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