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Complex Network Analysis in Python-The Pragmatic Programmer(2018).pdf
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资源说明：This book covers construction, exploration, analysis, and visualization of complex networks using NetworkX (a
Python library), as well as several other Python modules, and Gephi, an interactive environment for network
analysts. The book is not an introduction to Python. I assume that you already know the language, at least at the
level of a freshman programming course.
The book consists of five parts, each covering specific aspects of complex networks. Each part comes with one
or more detailed case studies.
Part I presents an overview of the main Python CNA modules: NetworkX, iGraph, graph-tool, and networkit.
It then goes over the construction of very simple networks both programmatically (using NetworkX) and
interactively (in Gephi), and it concludes by presenting a network of Wikipedia pages related to complex
networks.
In Part II, you’ll look into networks based on explicit relationships (such as social networks and communication
networks). This part addresses advanced network construction and measurement techniques. The capstone case
study—a network of “Panama papers”—illustrates possible money-laundering patterns in Central Asia.
Networks based on spatial and temporal co-occurrences—such as semantic and product networks—are the
subject of Part III. The third part also explores macroscopic and mesoscopic complex network structure. It paves
the way to network-based cultural domain analysis and a marketing study of Sephora cosmetic products.
If you cannot find any direct or indirect relationships between the items, but still would like to build a network
of them, the contents of Part IV come to the rescue. You will learn how to find out if items are similar, and you
will convert quantitative similarities into network edges. A network of psychological trauma types is one of the
outcomes of the fourth part.
The book concludes with Part V: directed networks with plenty of examples, including a network of qualitative
adjectives that you could use in computer games or fiction.
When you finish your journey, you’ll be able to identify, sketch (both by hand, in Gephi, and programmatically),
transform, analyze, and visualize several types of complex networks. You’ll be able to interpret network
measures and structure. The book doesn’t aim to be a comprehensive CNA reference. Many discipline-specific
aspects, such as triadic census, exponential random graph models (ERGMs), and network flows, as well as the
whole story of network dynamics (evolution and contagion), have been intentionally left uncharted. The
bibliography here will take you to more destinations of your choice, whether they be economic networks, web
scrapping, or classical social network analysis.
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