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Statistical Network Analysis Models Issues And New Directions
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Book Synopsis Statistical Network Analysis: Models, Issues, and New Directions by : Edoardo M. Airoldi
Download or read book Statistical Network Analysis: Models, Issues, and New Directions written by Edoardo M. Airoldi and published by Springer. This book was released on 2008-04-12 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Survey of Statistical Network Models by : Anna Goldenberg
Download or read book A Survey of Statistical Network Models written by Anna Goldenberg and published by Now Publishers Inc. This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Probabilistic Foundations of Statistical Network Analysis by : Harry Crane
Download or read book Probabilistic Foundations of Statistical Network Analysis written by Harry Crane and published by CRC Press. This book was released on 2018-04-17 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Bayesian Inference in the Social Sciences by : Ivan Jeliazkov
Download or read book Bayesian Inference in the Social Sciences written by Ivan Jeliazkov and published by John Wiley & Sons. This book was released on 2014-11-04 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
Book Synopsis Inferential Network Analysis by : Skyler J. Cranmer
Download or read book Inferential Network Analysis written by Skyler J. Cranmer and published by Cambridge University Press. This book was released on 2020-11-19 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.
Book Synopsis Handbook of Mixed Membership Models and Their Applications by : Edoardo M. Airoldi
Download or read book Handbook of Mixed Membership Models and Their Applications written by Edoardo M. Airoldi and published by CRC Press. This book was released on 2014-11-06 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, you’ll discover how to characterize complex multivariate data in: Studies involving genetic databases Patterns in the progression of diseases and disabilities Combinations of topics covered by text documents Political ideology or electorate voting patterns Heterogeneous relationships in networks, and much more The handbook spans more than 20 years of the editors’ and contributors’ statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.
Book Synopsis Statistical Analysis of Network Data with R by : Eric D. Kolaczyk
Download or read book Statistical Analysis of Network Data with R written by Eric D. Kolaczyk and published by Springer Nature. This book was released on 2020-06-02 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and 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. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.
Book Synopsis The Oxford Handbook of Political Networks by : Jennifer Nicoll Victor
Download or read book The Oxford Handbook of Political Networks written by Jennifer Nicoll Victor and published by Oxford University Press. This book was released on 2018 with total page 1011 pages. Available in PDF, EPUB and Kindle. Book excerpt: Politics is intuitively about relationships, but until recently the network perspective has not been a dominant part of the methodological paradigm that political scientists use to study politics. This volume is a foundational statement about networks in the study of politics.
Book Synopsis Social Network Analysis of Disaster Response, Recovery, and Adaptation by : Eric C Jones
Download or read book Social Network Analysis of Disaster Response, Recovery, and Adaptation written by Eric C Jones and published by Butterworth-Heinemann. This book was released on 2016-09-09 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Network Analysis of Disaster Response, Recovery, and Adaptation covers systematic social network analysis and how people and institutions function in disasters, after disasters, and the ways they adapt to hazard settings. As hazards become disasters, the opportunities and constraints for maintaining a safe and secure life and livelihood become too strained for many people. Anecdotally, and through many case studies, we know that social interactions exacerbate or mitigate those strains, necessitating a concerted, intellectual effort to understand the variation in how ties within, and outside, communities respond and are affected by hazards and disasters. Examines the role of societal relationships in a disaster context, incorporating theory and case studies by experts in the field Integrates research in the areas of social network analysis and inter-organizational networks Presents a range of studies from around the world, employing different approaches to network analysis in disaster contexts
Book Synopsis Structural Analysis of Complex Networks by : Matthias Dehmer
Download or read book Structural Analysis of Complex Networks written by Matthias Dehmer and published by Springer Science & Business Media. This book was released on 2010-10-14 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science, machine learning, artificial intelligence, computational and systems biology, cognitive science, computational linguistics, and mathematical chemistry. It may also be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods.
Book Synopsis Model-Based Clustering and Classification for Data Science by : Charles Bouveyron
Download or read book Model-Based Clustering and Classification for Data Science written by Charles Bouveyron and published by Cambridge University Press. This book was released on 2019-07-25 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
Book Synopsis Handbook of Cluster Analysis by : Christian Hennig
Download or read book Handbook of Cluster Analysis written by Christian Hennig and published by CRC Press. This book was released on 2015-12-16 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.The
Book Synopsis Advances in Network Clustering and Blockmodeling by : Patrick Doreian
Download or read book Advances in Network Clustering and Blockmodeling written by Patrick Doreian and published by John Wiley & Sons. This book was released on 2020-02-03 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling. Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more. Offers a clear and insightful look at the state of the art in network clustering and blockmodeling Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively Written by leading contributors in the field of spatial networks analysis Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
Book Synopsis Link Mining: Models, Algorithms, and Applications by : Philip S. Yu
Download or read book Link Mining: Models, Algorithms, and Applications written by Philip S. Yu and published by Springer Science & Business Media. This book was released on 2010-09-16 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.
Book Synopsis Social and Economic Networks by : Matthew O. Jackson
Download or read book Social and Economic Networks written by Matthew O. Jackson and published by Princeton University Press. This book was released on 2010-11-01 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks of relationships help determine the careers that people choose, the jobs they obtain, the products they buy, and how they vote. The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function. This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business.
Book Synopsis Social Network Analysis by : Mohammad Gouse Galety
Download or read book Social Network Analysis written by Mohammad Gouse Galety and published by John Wiley & Sons. This book was released on 2022-04-28 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: SOCIAL NETWORK ANALYSIS As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose. Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion. The book features: Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network. An understanding of network analysis and motivations to model phenomena as networks. Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted. Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption. Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems. The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research. The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers. Audience The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.
Book Synopsis Challenges in Social Network Research by : Giancarlo Ragozini
Download or read book Challenges in Social Network Research written by Giancarlo Ragozini and published by Springer Nature. This book was released on 2019-12-06 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book includes both invited and contributed chapters dealing with advanced methods and theoretical development for the analysis of social networks and applications in numerous disciplines. Some authors explore new trends related to network measures, multilevel networks and clustering on networks, while other contributions deepen the relationship among statistical methods for data mining and social network analysis. Along with the new methodological developments, the book offers interesting applications to a wide set of fields, ranging from the organizational and economic studies, collaboration and innovation, to the less usual field of poetry. In addition, the case studies are related to local context, showing how the substantive reasoning is fundamental in social network analysis. The list of authors includes both top scholars in the field of social networks and promising young researchers. All chapters passed a double blind review process followed by the guest editors. This edited volume will appeal to students, researchers and professionals.
