Know Python? Now, Learn Complex Network Analysis!

Complex network analysis (CNA) is a discipline of exploring quantitative relationships in the networks with non-trivial, irregular structure. The actual nature of the networks (social, semantic, transportation, communication, economic, and the like) does not matter, as long as their organization is sufficiently random.

The beta version of my book Complex Network Analysis in Python. Recognize → Construct → Visualize → Analyze → Interpret has been published by The Pragmatic Bookshelf. The book mostly covers NetworkX. It is intended for curious Python programmers, data scientists, and complex network analysis specialists. The book assumes that you have some background in Python programming. It expects from you no more than common sense knowledge of complex networks. The intention is to build up your CNA programming skills and at the same time educate you about the elements of CNA itself. If you are an experienced Python programmer, you can devote more attention to the CNA techniques. On the contrary, if you are 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 CNA intuition to grasp programming concepts.

The book covers:

• Introduction to complex networks
• Social networks
• Semantic and product networks
• Similarity-based networks
• Bipartite networks
• Directed networks
• NetworkX (extensively) and Gephi (in a sketchy way)
• Five case studies with full code, including cultural domain analysis

The beta version of the book is available from the publisher's website (https://lnkd.in/eKm6EtM). The page has links to book excerpts, code fragments, forum, and errata.