In the past decade, the study of networks has increased dramatically. Researchers from across the sciences—including biology and bioinformatics, computer science, economics, engineering, mathematics, physics, sociology, and statistics—are more and more involved with the collection and statistical analysis of network-indexed data. As a result, statistical methods and models are being developed in this area at a furious pace, with contributions coming from a wide spectrum of disciplines.
This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines. The material is organized according to a statistical taxonomy, although the presentation entails a conscious balance of concepts versus mathematics. In addition, the examples—including extended cases studies—are drawn widely from the literature. This book should be of substantial interest both to statisticians and to anyone else working in the area of ‘network science.’
The coverage of topics in this book is broad, but unfolds in a systematic manner, moving from descriptive (or exploratory) methods, to sampling, to modeling and inference. Specific topics include network mapping, characterization of network structure, network sampling, and the modeling, inference, and prediction of networks, network processes, and network flows. This book is the first such resource to present material on all of these core topics in one place.
具體描述
讀後感
有限的存在如井底之蛙，因而，人生若充满好奇，所见之世界必然充满了惊喜。写这么少显然不能作为评论的，但我强烈推荐任何自认为对于网络研究入门的人用来鉴别自己的等级。这本书中有很多惊喜。我会以之作为这半年主要的研究方向。
評分有限的存在如井底之蛙，因而，人生若充满好奇，所见之世界必然充满了惊喜。写这么少显然不能作为评论的，但我强烈推荐任何自认为对于网络研究入门的人用来鉴别自己的等级。这本书中有很多惊喜。我会以之作为这半年主要的研究方向。
評分有限的存在如井底之蛙，因而，人生若充满好奇，所见之世界必然充满了惊喜。写这么少显然不能作为评论的，但我强烈推荐任何自认为对于网络研究入门的人用来鉴别自己的等级。这本书中有很多惊喜。我会以之作为这半年主要的研究方向。
評分有限的存在如井底之蛙，因而，人生若充满好奇，所见之世界必然充满了惊喜。写这么少显然不能作为评论的，但我强烈推荐任何自认为对于网络研究入门的人用来鉴别自己的等级。这本书中有很多惊喜。我会以之作为这半年主要的研究方向。
評分有限的存在如井底之蛙，因而，人生若充满好奇，所见之世界必然充满了惊喜。写这么少显然不能作为评论的，但我强烈推荐任何自认为对于网络研究入门的人用来鉴别自己的等级。这本书中有很多惊喜。我会以之作为这半年主要的研究方向。
用戶評價
對已有工作的總結非常精當，數學不好者慎重⋯⋯
评分對已有工作的總結非常精當，數學不好者慎重⋯⋯
评分對已有工作的總結非常精當，數學不好者慎重⋯⋯
评分對已有工作的總結非常精當，數學不好者慎重⋯⋯
评分對已有工作的總結非常精當，數學不好者慎重⋯⋯
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