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In science, technology, and mathematics, a network is a system of interconnected objects. 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) doesn’t matter, as long as their organization doesn’t reveal any specific patterns. This book was inspired by a decade of CNA practice and research. Being a professor of mathematics and computer science at Suffolk University in Boston, I have experimented with complex networks of various sizes, purposes, and origins. I developed my first CNA software in an ad hoc manner in the C language—the language venerable yet ill-suited for CNA projects. The price of explicit memory management, cumbersome file input/output, and lack of advanced built-in data structures (such as maps and lists) was simply too high to justify a further commitment to C. At the moment I realized that there were affordable alternatives to C that did not require low-level programming (such as Pajek [NMB11] and Mathematica1), off I went. Both systems that I mentioned had significant restrictions. Mathematica was proprietary (and, frankly, quite costly). My inner open source advocate demanded that I cease and desist using it, especially given that earlier versions of Mathematica didn’t provide dedicated CNA support and failed to handle big networks. Pajek was proprietary, too, and not programmable. It took a joint effort of my inner open source advocate and inner programmer to push it to the periphery. (I still occasionally use Pajek, and I believe it’s a great system for solving non-recurring problems.) I felt delighted when, in search of open source, free, scalable, reliable, and programmable CNA software, I ran into NetworkX, a Python library still in its infancy. For the next several years, it became my tool of choice when it came to CNA simulation, analysis, or visualization.