Complex Network Analysis in Python下载

weixin_39821228 2020-06-19 01:00:29

This book is an excellent read for anyone who wants to learn the fundamentals of complex network analysis
with a focus on application. The case studies cover a variety of topics and help readers link concepts to
applications, providing readers with a clear, well-structured, hands-on experience that
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This book is an excellent read for anyone who wants to learn the fundamentals of complex network analysis with a focus on application. The case studies cover a variety of topics and help readers link concepts to applications, providing readers with a clear, well-structured, hands-on experience that deepens their understanding of the concepts without requiring Python programming experience. This book is intended for graduate and undergraduate students, complex data analysis (CNA) or social network analysis (SNA) instructors, and CNA/SNA researchers and practitioners. The book assumes that you have some background in computer programming—namely, 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’re an experienced Python programmer, you can devote more attention to the CNA techniques. On the contrary, if you’re 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.

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

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Complex Network Analysis in Python Recognize – Construct – Visualize – Analyze – Interpret 2018edition

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