Statistical Analysis Of Graph Structures In Random Variable Networks

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Statistical Analysis of Graph Structures in Random Variable Networks

V. A. Kalyagin,A. P. Koldanov,P. A. Koldanov,P. M. Pardalos
Statistical Analysis of Graph Structures in Random Variable Networks Book PDF

Author : V. A. Kalyagin,A. P. Koldanov,P. A. Koldanov,P. M. Pardalos
Publisher : Springer Nature
Page : 101 pages
File Size : 42,6 Mb
Release : 2020-12-05
Category : Mathematics
ISBN : 9783030602932

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Statistical Analysis of Graph Structures in Random Variable Networks by V. A. Kalyagin,A. P. Koldanov,P. A. Koldanov,P. M. Pardalos Pdf

This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.

Statistical Analysis of Network Data with R

Eric D. Kolaczyk,Gábor Csárdi
Statistical Analysis of Network Data with R Book PDF

Author : Eric D. Kolaczyk,Gábor Csárdi
Publisher : Springer
Page : 207 pages
File Size : 49,7 Mb
Release : 2014-05-22
Category : Computers
ISBN : 9781493909834

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Statistical Analysis of Network Data with R by Eric D. Kolaczyk,Gábor Csárdi Pdf

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).

Statistical Analysis of Network Data

Eric D. Kolaczyk
Statistical Analysis of Network Data Book PDF

Author : Eric D. Kolaczyk
Publisher : Springer Science & Business Media
Page : 397 pages
File Size : 44,5 Mb
Release : 2009-04-20
Category : Computers
ISBN : 9780387881461

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Statistical Analysis of Network Data by Eric D. Kolaczyk Pdf

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.

Big Data of Complex Networks

Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl,Andreas Holzinger
Big Data of Complex Networks Book PDF

Author : Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl,Andreas Holzinger
Publisher : CRC Press
Page : 290 pages
File Size : 41,8 Mb
Release : 2016-08-19
Category : Computers
ISBN : 9781315353593

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Big Data of Complex Networks by Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl,Andreas Holzinger Pdf

Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: Provides a complete discussion of both the hardware and software used to organize big data Describes a wide range of useful applications for managing big data and resultant data sets Maintains a firm focus on massive data and large networks Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.

Random Graphs and Complex Networks

Remco van der Hofstad
Random Graphs and Complex Networks Book PDF

Author : Remco van der Hofstad
Publisher : Cambridge University Press
Page : 341 pages
File Size : 50,8 Mb
Release : 2016-12-22
Category : Computers
ISBN : 9781107172876

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Random Graphs and Complex Networks by Remco van der Hofstad Pdf

This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises.

Mathematical Optimization Theory and Operations Research: Recent Trends

Michael Khachay,Yury Kochetov,Anton Eremeev,Oleg Khamisov,Vladimir Mazalov,Panos Pardalos
Mathematical Optimization Theory and Operations Research Recent Trends Book PDF

Author : Michael Khachay,Yury Kochetov,Anton Eremeev,Oleg Khamisov,Vladimir Mazalov,Panos Pardalos
Publisher : Springer Nature
Page : 412 pages
File Size : 48,7 Mb
Release : 2023-09-20
Category : Mathematics
ISBN : 9783031432576

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Mathematical Optimization Theory and Operations Research: Recent Trends by Michael Khachay,Yury Kochetov,Anton Eremeev,Oleg Khamisov,Vladimir Mazalov,Panos Pardalos Pdf

This book constitutes refereed proceedings of the 22nd International Conference on Mathematical Optimization Theory and Operations Research: Recent Trends, MOTOR 2023, held in Ekaterinburg, Russia, during July 2–8, 2023. The 28 full papers and one invited paper presented in this volume were carefully reviewed and selected from a total of 61 submissions. The papers in the volume are organized according to the following topical headings: mathematical programming; stochastic optimization; discrete and combinatorial optimization; operations research; optimal control and mathematical economics; and optimization in machine learning.

Probabilistic Foundations of Statistical Network Analysis

Harry Crane
Probabilistic Foundations of Statistical Network Analysis Book PDF

Author : Harry Crane
Publisher : CRC Press
Page : 432 pages
File Size : 52,7 Mb
Release : 2018-04-17
Category : Business & Economics
ISBN : 9781351807326

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Probabilistic Foundations of Statistical Network Analysis by Harry Crane Pdf

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

The SAGE Handbook of Social Network Analysis

John Scott,Peter J. Carrington
The SAGE Handbook of Social Network Analysis Book PDF

Author : John Scott,Peter J. Carrington
Publisher : SAGE Publications
Page : 641 pages
File Size : 47,8 Mb
Release : 2011-05-25
Category : Social Science
ISBN : 9781847873958

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The SAGE Handbook of Social Network Analysis by John Scott,Peter J. Carrington Pdf

This sparkling Handbook offers an unrivalled resource for those engaged in the cutting edge field of social network analysis. Systematically, it introduces readers to the key concepts, substantive topics, central methods and prime debates. Among the specific areas covered are: Network theory Interdisciplinary applications Online networks Corporate networks Lobbying networks Deviant networks Measuring devices Key Methodologies Software applications. The result is a peerless resource for teachers and students which offers a critical survey of the origins, basic issues and major debates. The Handbook provides a one-stop guide that will be used by readers for decades to come.

Methods of Microarray Data Analysis

Simon M. Lin,Kimberly F. Johnson
Methods of Microarray Data Analysis Book PDF

Author : Simon M. Lin,Kimberly F. Johnson
Publisher : Springer Science & Business Media
Page : 192 pages
File Size : 40,7 Mb
Release : 2012-12-06
Category : Science
ISBN : 9781461508731

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Methods of Microarray Data Analysis by Simon M. Lin,Kimberly F. Johnson Pdf

Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis is one of the first books dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods ranging from data normalization, feature selection and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis focuses on two well-known data sets, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.

A Survey of Statistical Network Models

Anna Goldenberg,Alice X. Zheng,Stephen E. Fienberg,Edoardo M. Airoldi
A Survey of Statistical Network Models Book PDF

Author : Anna Goldenberg,Alice X. Zheng,Stephen E. Fienberg,Edoardo M. Airoldi
Publisher : Now Publishers Inc
Page : 118 pages
File Size : 41,5 Mb
Release : 2010
Category : Computers
ISBN : 9781601983206

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A Survey of Statistical Network Models by Anna Goldenberg,Alice X. Zheng,Stephen E. Fienberg,Edoardo M. Airoldi Pdf

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.

Functional and High-Dimensional Statistics and Related Fields

Germán Aneiros,Ivana Horová,Marie Hušková,Philippe Vieu
Functional and High Dimensional Statistics and Related Fields Book PDF

Author : Germán Aneiros,Ivana Horová,Marie Hušková,Philippe Vieu
Publisher : Springer Nature
Page : 254 pages
File Size : 47,7 Mb
Release : 2020-06-19
Category : Mathematics
ISBN : 9783030477561

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Functional and High-Dimensional Statistics and Related Fields by Germán Aneiros,Ivana Horová,Marie Hušková,Philippe Vieu Pdf

This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field.

Data Analysis, Classification, and Related Methods

Henk A.L. Kiers,Jean-Paul Rasson,Patrick J.F. Groenen,Martin Schader
Data Analysis Classification and Related Methods Book PDF

Author : Henk A.L. Kiers,Jean-Paul Rasson,Patrick J.F. Groenen,Martin Schader
Publisher : Springer Science & Business Media
Page : 428 pages
File Size : 40,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9783642597893

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Data Analysis, Classification, and Related Methods by Henk A.L. Kiers,Jean-Paul Rasson,Patrick J.F. Groenen,Martin Schader Pdf

This volume contains a selection of papers presented at the Seven~h Confer ence of the International Federation of Classification Societies (IFCS-2000), which was held in Namur, Belgium, July 11-14,2000. From the originally sub mitted papers, a careful review process involving two reviewers per paper, led to the selection of 65 papers that were considered suitable for publication in this book. The present book contains original research contributions, innovative ap plications and overview papers in various fields within data analysis, classifi cation, and related methods. Given the fast publication process, the research results are still up-to-date and coincide with their actual presentation at the IFCS-2000 conference. The topics captured are: • Cluster analysis • Comparison of clusterings • Fuzzy clustering • Discriminant analysis • Mixture models • Analysis of relationships data • Symbolic data analysis • Regression trees • Data mining and neural networks • Pattern recognition • Multivariate data analysis • Robust data analysis • Data science and sampling The IFCS (International Federation of Classification Societies) The IFCS promotes the dissemination of technical and scientific information data analysis, classification, related methods, and their applica concerning tions.

Topics at the Frontier of Statistics and Network Analysis

Eric D. Kolaczyk
Topics at the Frontier of Statistics and Network Analysis Book PDF

Author : Eric D. Kolaczyk
Publisher : Cambridge University Press
Page : 214 pages
File Size : 40,8 Mb
Release : 2017-08-10
Category : Mathematics
ISBN : 9781108305617

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Topics at the Frontier of Statistics and Network Analysis by Eric D. Kolaczyk Pdf

This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done.

Random Graphs and Complex Networks

Remco van der Hofstad
Random Graphs and Complex Networks Book PDF

Author : Remco van der Hofstad
Publisher : Unknown
Page : 321 pages
File Size : 49,9 Mb
Release : 2017
Category : Electronic
ISBN : 1316625060

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Random Graphs and Complex Networks by Remco van der Hofstad Pdf

This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models.

Bayesian Networks

Marco Scutari,Jean-Baptiste Denis
Bayesian Networks Book PDF

Author : Marco Scutari,Jean-Baptiste Denis
Publisher : CRC Press
Page : 243 pages
File Size : 41,6 Mb
Release : 2014-06-20
Category : Computers
ISBN : 9781482225587

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Bayesian Networks by Marco Scutari,Jean-Baptiste Denis Pdf

Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.