Network Models for Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Network Models for Data Science write by Alan Julian Izenman. This book was released on 2022-12-31. Network Models for Data Science available in PDF, EPUB and Kindle. This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.
A Survey of Statistical Network Models
A Survey of Statistical Network Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Survey of Statistical Network Models write by Anna Goldenberg. This book was released on 2010. A Survey of Statistical Network Models available in PDF, EPUB and Kindle. Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.
Statistical Analysis of Network Data
Statistical Analysis of Network Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Analysis of Network Data write by Eric D. Kolaczyk. This book was released on 2009-04-20. Statistical Analysis of Network Data available in PDF, EPUB and Kindle. In recent years there has been an explosion of network data – that is, measu- ments that are either of or from a system conceptualized as a network – from se- ingly all corners of science. The combination of an increasingly pervasive interest in scienti c analysis at a systems level and the ever-growing capabilities for hi- throughput data collection in various elds has fueled this trend. Researchers from biology and bioinformatics to physics, from computer science to the information sciences, and from economics to sociology are more and more engaged in the c- lection and statistical analysis of data from a network-centric perspective. Accordingly, the contributions to statistical methods and modeling in this area have come from a similarly broad spectrum of areas, often independently of each other. Many books already have been written addressing network data and network problems in speci c individual disciplines. However, there is at present no single book that provides a modern treatment of a core body of knowledge for statistical analysis of network data that cuts across the various disciplines and is organized rather according to a statistical taxonomy of tasks and techniques. This book seeks to ll that gap and, as such, it aims to contribute to a growing trend in recent years to facilitate the exchange of knowledge across the pre-existing boundaries between those disciplines that play a role in what is coming to be called ‘network science.
Algorithms and Models for Network Data and Link Analysis
Algorithms and Models for Network Data and Link Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algorithms and Models for Network Data and Link Analysis write by François Fouss. This book was released on 2016-07-12. Algorithms and Models for Network Data and Link Analysis available in PDF, EPUB and Kindle. Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. MATLAB®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.
Data Science and Complex Networks
Data Science and Complex Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Science and Complex Networks write by Guido Caldarelli. This book was released on 2016-11-10. Data Science and Complex Networks available in PDF, EPUB and Kindle. This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to definition and implementation of the most important network models. The text provides information on the structure of the data and on the quality of available datasets. Furthermore it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose the authors have set up a dedicated web site where readers can download and test the codes. The whole project is aimed as a learning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks.
