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| Author | : Alan Julian Izenman |
| Publisher | : Cambridge University Press |
| Total Pages | : 501 |
| Release | : 2022-12-31 |
| Genre | : Mathematics |
| ISBN | : 1108835767 |
This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.
| Author | : Anna Goldenberg |
| Publisher | : Now Publishers Inc |
| Total Pages | : 118 |
| Release | : 2010 |
| Genre | : Computers |
| ISBN | : 1601983204 |
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.
| Author | : Eric D. Kolaczyk |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 397 |
| Release | : 2009-04-20 |
| Genre | : Computers |
| ISBN | : 0387881468 |
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.
| Author | : François Fouss |
| Publisher | : Cambridge University Press |
| Total Pages | : 549 |
| Release | : 2016-07-12 |
| Genre | : Computers |
| ISBN | : 1316712516 |
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.
| Author | : Xin W. Chen |
| Publisher | : Springer Nature |
| Total Pages | : 126 |
| Release | : 2022-02-21 |
| Genre | : Technology & Engineering |
| ISBN | : 3030964701 |
This book explains network science and its applications in data analytics for critical infrastructures, engineered systems, and knowledge acquisition. Each chapter describes step-by-step processes of how network science enables and automates data analytics through examples. The book not only dissects modeling techniques and analytical results but also explores the intrinsic development of these models and analyses. This unique approach bridges the gap between theory and practice and channels’ managerial and problem-solving skills. Engineers, researchers, and managers would benefit from the extensive theoretical background and practical examples discussed in this book. Advanced undergraduate students and graduate students in mathematics, statistics, engineering, business, public health, and social science may use this book as a one-semester textbook or a reference book. Readers who are more interested in applications may skip Chapter 1 and peruse through the rest of the book with ease.
| Author | : Guido Caldarelli |
| Publisher | : Oxford University Press |
| Total Pages | : 136 |
| Release | : 2016-11-10 |
| Genre | : Science |
| ISBN | : 0191024023 |
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.
| Author | : Eric D. Kolaczyk |
| Publisher | : Springer |
| Total Pages | : 214 |
| Release | : 2014-05-22 |
| Genre | : Computers |
| ISBN | : 1493909835 |
Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).
| Author | : Mitsuo Gen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 692 |
| Release | : 2008-07-10 |
| Genre | : Technology & Engineering |
| ISBN | : 1848001819 |
Network models are critical tools in business, management, science and industry. “Network Models and Optimization” presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. The book extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, traveling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. The book can be used both as a student textbook and as a professional reference for practitioners who use network optimization methods to model and solve problems.
| Author | : Edoardo M. Airoldi |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 203 |
| Release | : 2007-07-20 |
| Genre | : Computers |
| ISBN | : 3540731326 |
This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.
| Author | : Yulius Yus |
| Publisher | : AMERTA MEDIA |
| Total Pages | : 122 |
| Release | : 2024-08-12 |
| Genre | : Antiques & Collectibles |
| ISBN | : 6234196721 |
There is an old saying: "It is easier to wake someone who is sound asleep than to wake who is pretending to sleep." As network administrators, competing with Al systems in the future is not an impossibility. Analyzing events with simple examples like comparing bandwidth, throughput, jitter, and optimizing server resource usage can be exhausting when applied to dozens or hundreds of devices. For instance, how do we classify when the bandwidth is low in one place but normal elsewhere based on several sample events? Or how do we classify which servers need maintenance and which do not, based on existing data parameters, referring to unsupervised and supervised learning? Classification models can indeed simplify and optimize our work, saving time compared to performing network maintenance one by one, using only Google Colab. In this context, we have tried using a small of unsupervised and supervised learning to process data from simple network case studies. These data are analyzed for classification, regression, and prediction using Python scripts implemented in Google Colab. This book serves as a starting point to be developed into a continuous series, extending to supervised learning, deep learning, and reinforcement learning, which are still in the testing and writing phase. We hope this new approach benefits network administrators wherever you are.
