Python中的复杂网络分析实战

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"Complex Network Analysis in Python 是一本专注于复杂网络分析基础的书籍,通过各种案例研究,帮助读者将概念与应用联系起来,提供清晰、结构化的实践经验。作者Dmitry Zinoviev在书中引导读者从识别、构建、可视化、分析到解释网络的全过程。" 在《Python中的复杂网络分析》一书中,作者旨在引领读者进入复杂网络分析的世界。书的结构设计为五个主要步骤:认识、构建、可视化、分析和解释。这五个步骤涵盖了从理解网络的本质到运用工具进行实际分析的整个过程。 第一部分——基础网络和工具,作者首先介绍了这一领域的基本概念,并对常用工具进行了概述。他告诫读者不要自行编织网络,而是推荐使用已有的工具。其中,提到了几个关键的Python库,如iGraph,它是一个强大的图形处理库;graph-tool,其性能强大,但可能需要一定的学习曲线;NetworkX,这是一个广泛使用的网络分析库,适合初学者入门;以及NetworKit,一个功能丰富的开源工具,适合更高级的分析。作者还提醒读者对比这些工具,根据个人需求选择最适合的。 第二部分深入探讨了NetworkX,这是Python中最常用的网络分析库。读者将学习如何使用NetworkX构建简单的网络,添加属性,如节点和边的属性,以及如何使用Matplotlib进行网络可视化,以便更好地理解和交流网络结构。此外,还介绍了如何保存和共享网络数据,这对于协作和重复使用分析结果至关重要。 第三部分,作者引入了Gephi,这是一个图形化的网络分析和可视化软件。Gephi不仅能够导入和修改网络,还能帮助用户探索和描绘网络的结构。通过Gephi,即使非编程背景的读者也能直观地理解网络的特征,进行交互式的探索和布局优化。 这本书为读者提供了全面了解和实践复杂网络分析的平台,结合理论与实践,通过具体的案例和工具使用,使读者能够逐步掌握这个领域的核心技能。无论是对于学术研究,还是在数据科学、社交网络分析等领域的工作,这本书都将是一份宝贵的资源。
2018-04-04 上传
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