Author : Alan Julian Izenman
Publisher : Cambridge University Press
Page : 501 pages
File Size : 28,74 MB
Release : 2022-12-31
Category : Mathematics
ISBN : 1108835767
This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.
Author : Mitsuo Gen
Publisher : Springer Science & Business Media
Page : 692 pages
File Size : 28,60 MB
Release : 2008-07-10
Category : 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 : Fred Glover
Publisher : John Wiley & Sons
Page : 306 pages
File Size : 26,97 MB
Release : 2011-10-14
Category : Mathematics
ISBN : 1118031423
Unique in that it focuses on formulation and case studies ratherthan solutions procedures covering applications for pure,generalized and integer networks, equivalent formulations plussuccessful techniques of network models. Every chapter contains asimple model which is expanded to handle more complicateddevelopments, a synopsis of existing applications, one or more casestudies, at least 20 exercises and invaluable references. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available upon request from the Wileyeditorial department.
Author : Jan Treur
Publisher : Springer
Page : 501 pages
File Size : 40,82 MB
Release : 2016-10-03
Category : Science
ISBN : 3319452134
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.
Author : NARAYAN CHANGDER
Publisher : CHANGDER OUTLINE
Page : 36 pages
File Size : 22,50 MB
Release : 1998-01-01
Category : Mathematics
ISBN :
THE NETWORK MODELS MCQ (MULTIPLE CHOICE QUESTIONS) SERVES AS A VALUABLE RESOURCE FOR INDIVIDUALS AIMING TO DEEPEN THEIR UNDERSTANDING OF VARIOUS COMPETITIVE EXAMS, CLASS TESTS, QUIZ COMPETITIONS, AND SIMILAR ASSESSMENTS. WITH ITS EXTENSIVE COLLECTION OF MCQS, THIS BOOK EMPOWERS YOU TO ASSESS YOUR GRASP OF THE SUBJECT MATTER AND YOUR PROFICIENCY LEVEL. BY ENGAGING WITH THESE MULTIPLE-CHOICE QUESTIONS, YOU CAN IMPROVE YOUR KNOWLEDGE OF THE SUBJECT, IDENTIFY AREAS FOR IMPROVEMENT, AND LAY A SOLID FOUNDATION. DIVE INTO THE NETWORK MODELS MCQ TO EXPAND YOUR NETWORK MODELS KNOWLEDGE AND EXCEL IN QUIZ COMPETITIONS, ACADEMIC STUDIES, OR PROFESSIONAL ENDEAVORS. THE ANSWERS TO THE QUESTIONS ARE PROVIDED AT THE END OF EACH PAGE, MAKING IT EASY FOR PARTICIPANTS TO VERIFY THEIR ANSWERS AND PREPARE EFFECTIVELY.
Author : Eric D. Kolaczyk
Publisher : Springer Science & Business Media
Page : 397 pages
File Size : 37,23 MB
Release : 2009-04-20
Category : 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 : Alan Julian Izenman
Publisher : Cambridge University Press
Page : 502 pages
File Size : 43,15 MB
Release : 2023-01-05
Category : Mathematics
ISBN : 1108889034
This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.
Author : Barry Libert
Publisher : Harvard Business Review Press
Page : 247 pages
File Size : 20,43 MB
Release : 2016-06-07
Category : Business & Economics
ISBN : 163369206X
Pivot your organization toward a more scalable and profitable business model. Digital networks are changing all the rules of business. New, scalable, digitally networked business models, like those of Amazon, Google, Uber, and Airbnb, are affecting growth, scale, and profit potential for companies in every industry. But this seismic shift isn’t unique to digital start-ups and tech superstars. Digital transformation is affecting every business sector, and as investor capital, top talent, and customers shift toward network-centric organizations, the performance gap between early and late adopters is widening. So the question isn’t whether your organization needs to change, but when and how much. The Network Imperative is a call to action for managers and executives to embrace network-based business models. The benefits are indisputable: companies that leverage digital platforms to co-create and share value with networks of employees, customers, and suppliers are fast outpacing the market. These companies, or network orchestrators, grow faster, scale with lower marginal cost, and generate the highest revenue multipliers. Supported by research that covers fifteen hundred companies, authors Barry Libert, Megan Beck, and Jerry Wind guide leaders and investors through the ten principles that all organizations can use to grow and profit regardless of their industry. They also share a five-step process for pivoting an organization toward a more scalable and profitable business model. The Network Imperative, brimming with compelling case studies and actionable advice, provides managers with what they really need: new tools and frameworks to generate unprecedented value in a rapidly changing age.
Author : E. R. Lewis
Publisher : Springer Science & Business Media
Page : 414 pages
File Size : 32,88 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 3642811345
This book is an outgrowth of one phase of an upper-division course on quantitative ecology, given each year for the past eight at Berkeley. I am most grateful to the students in that course and to many graduate students in the Berkeley Department of Zoology and Colleges of Engineering and Natural Resources whose spirited discussions inspired much of the book's content. I also am deeply grateful to those faculty colleagues with whom, at one time or another, I have shared courses or seminars in ecology or population biology, D.M. Auslander, L. Demetrius, G. Oster, O.H. Paris, F.A. Pitelka, A.M. Schultz, Y. Takahashi, D.B. Tyler, and P. Vogelhut, all of whom contributed substantially to the development of my thinking in those fields, to my Depart mental colleagues E. Polak and A.J. Thomasian, who guided me into the litera ture on numerical methods and stochastic processes, and to the graduate students who at one time or another have worked with me on population-biology projects, L.M. Brodnax, S-P. Chan, A. Elterman, G.C. Ferrell, D. Green, C. Hayashi, K-L. Lee, W.F. Martin Jr., D. May, J. Stamnes, G.E. Swanson, and I. Weeks, who, together, undoubtedly provided me with the greatest inspiration. I am indebted to the copy-editing and production staff of Springer-Verlag, especially to Ms. M. Muzeniek, for their diligence and skill, and to Mrs. Alice Peters, biomathematics editor, for her patience.
Author : Anna Goldenberg
Publisher : Now Publishers Inc
Page : 118 pages
File Size : 30,30 MB
Release : 2010
Category : 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.
