This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.

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.

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.

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.

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.

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.

This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master’s and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.