Models and Methods in Social Network Analysis

Models and Methods in Social Network Analysis

Author: Peter J. Carrington

Publisher:

Published: 2005-02-07

Total Pages: 328

ISBN-13: 9780521809597

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Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or 'two-mode' networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.


Network Analysis and Synthesis

Network Analysis and Synthesis

Author: Brian D. O. Anderson

Publisher: Courier Corporation

Published: 2013-01-30

Total Pages: 559

ISBN-13: 0486152170

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This comprehensive look at linear network analysis and synthesis explores state-space synthesis as well as analysis, employing modern systems theory to unite classical concepts of network theory. 1973 edition.


A Survey of Statistical Network Models

A Survey of Statistical Network Models

Author: Anna Goldenberg

Publisher: Now Publishers Inc

Published: 2010

Total Pages: 118

ISBN-13: 1601983204

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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.


Semi-empirical Neural Network Modeling and Digital Twins Development

Semi-empirical Neural Network Modeling and Digital Twins Development

Author: Dmitriy Tarkhov

Publisher: Academic Press

Published: 2019-11-23

Total Pages: 290

ISBN-13: 012815652X

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Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion of task parameters in the final solution formula, which eliminates the need for repeated problem-solving. This is especially important for constructing individual models with unique features. The book illustrates key concepts through a large number of specific problems, both hypothetical models and practical interest. - Offers a new approach to neural networks using a unified simulation model at all stages of design and operation - Illustrates this new approach with numerous concrete examples throughout the book - Presents the methodology in separate and clearly-defined stages


Handbook on Modelling for Discrete Optimization

Handbook on Modelling for Discrete Optimization

Author: Gautam M. Appa

Publisher: Springer Science & Business Media

Published: 2006-08-18

Total Pages: 443

ISBN-13: 0387329420

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This book aims to demonstrate and detail the pervasive nature of Discrete Optimization. The handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It is done with an academic treatment outlining the state-of-the-art for researchers across the domains of the Computer Science, Math Programming, Applied Mathematics, Engineering, and Operations Research. The book utilizes the tools of mathematical modeling, optimization, and integer programming to solve a broad range of modern problems.


Network-Oriented Modeling

Network-Oriented Modeling

Author: Jan Treur

Publisher: Springer

Published: 2016-10-03

Total Pages: 501

ISBN-13: 3319452134

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This book presents a new approach that can be applied to complex, integrated individual and social human processes. It provides an alternative means of addressing complexity, better suited for its purpose than and effectively complementing traditional strategies involving isolation and separation assumptions. Network-oriented modeling allows high-level cognitive, affective and social models in the form of (cyclic) graphs to be constructed, which can be automatically transformed into executable simulation models. The modeling format used makes it easy to take into account theories and findings about complex cognitive and social processes, which often involve dynamics based on interrelating cycles. Accordingly, it makes it possible to address complex phenomena such as the integration of emotions within cognitive processes of all kinds, of internal simulations of the mental processes of others, and of social phenomena such as shared understandings and collective actions. A variety of sample models – including those for ownership of actions, fear and dreaming, the integration of emotions in joint decision-making based on empathic understanding, and evolving social networks – illustrate the potential of the approach. Dedicated software is available to support building models in a conceptual or graphical manner, transforming them into an executable format and performing simulation experiments. The majority of the material presented has been used and positively evaluated by undergraduate and graduate students and researchers in the cognitive, social and AI domains. Given its detailed coverage, the book is ideally suited as an introduction for graduate and undergraduate students in many different multidisciplinary fields involving cognitive, affective, social, biological, and neuroscience domains.


Statistical Network Analysis: Models, Issues, and New Directions

Statistical Network Analysis: Models, Issues, and New Directions

Author: Edoardo M. Airoldi

Publisher: Springer

Published: 2008-04-12

Total Pages: 204

ISBN-13: 3540731334

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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.


Weighted Network Analysis

Weighted Network Analysis

Author: Steve Horvath

Publisher: Springer Science & Business Media

Published: 2011-04-30

Total Pages: 433

ISBN-13: 144198819X

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High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.


Transportation Network Modeling and Calibration

Transportation Network Modeling and Calibration

Author: Mansoureh Jeihani

Publisher: Momentum Press

Published: 2017-09-11

Total Pages: 140

ISBN-13: 1606508946

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This book introduces transportation engineering students and junior engineers to the concept of transportation network modeling, network coding, model calibration and validation, and model evaluation. Transportation scientists employ modeling and simulation techniques to capture the complexities of transportation systems and develop and assess solutions to alleviate existing and future transportation-related problems. This book introduces transportation engineering students and junior engineers to the concept of transportation network modeling, network coding, model calibration and validation, and model evaluation. Travel demand models are sensitive to demographic changes and can explain and forecast how a new transportation supply system leads to a new transportation demand pattern. This book also describes how demand models evolved from trip-based to the newer generation of activity-based and agent-based to overcome some of the shortcomings of the four-step approach and improve models’ prediction power.


Network Simulation

Network Simulation

Author: Richard M. Fujimoto

Publisher: Morgan & Claypool Publishers

Published: 2007

Total Pages: 73

ISBN-13: 1598291106

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"Network Simulation" presents a detailed introduction to the design, implementation, and use of network simulation tools. Discussion topics include the requirements and issues faced for simulator design and use in wired networks, wireless networks, distributed simulation environments, and fluid model abstractions. Several existing simulations are given as examples, with details regarding design decisions and why those decisions were made. Issues regarding performance and scalability are discussed in detail, describing how one can utilize distributed simulation methods to increase the.