A comprehensive look at the emerging science of networks Network science helps you design faster, more resilient communication networks; revise infrastructure systems such as electrical power grids, telecommunications networks, and airline routes; model market dynamics; understand synchronization in biological systems; and analyze social interactions among people. This is the first book to take a comprehensive look at this emerging science. It examines the various kinds of networks (regular, random, small-world, influence, scale-free, and social) and applies network processes and behaviors to emergence, epidemics, synchrony, and risk. The book's uniqueness lies in its integration of concepts across computer science, biology, physics, social network analysis, economics, and marketing. The book is divided into easy-to-understand topical chapters and the presentation is augmented with clear illustrations, problems and answers, examples, applications, tutorials, and a discussion of related Java software. Chapters cover: Origins Graphs Regular Networks Random Networks Small-World Networks Scale-Free Networks Emergence Epidemics Synchrony Influence Networks Vulnerability Net Gain Biology This book offers a new understanding and interpretation of the field of network science. It is an indispensable resource for researchers, professionals, and technicians in engineering, computing, and biology. It also serves as a valuable textbook for advanced undergraduate and graduate courses in related fields of study.
Ernesto Estrada,Maria Fox,Desmond J. Higham,Gian-Luca Oppo
Author: Ernesto Estrada,Maria Fox,Desmond J. Higham,Gian-Luca Oppo
Publisher: Springer Science & Business Media
Network Science is the emerging field concerned with the study of large, realistic networks. This interdisciplinary endeavor, focusing on the patterns of interactions that arise between individual components of natural and engineered systems, has been applied to data sets from activities as diverse as high-throughput biological experiments, online trading information, smart-meter utility supplies, and pervasive telecommunications and surveillance technologies. This unique text/reference provides a fascinating insight into the state of the art in network science, highlighting the commonality across very different areas of application and the ways in which each area can be advanced by injecting ideas and techniques from another. The book includes contributions from an international selection of experts, providing viewpoints from a broad range of disciplines. It emphasizes networks that arise in nature—such as food webs, protein interactions, gene expression, and neural connections—and in technology—such as finance, airline transport, urban development and global trade. Topics and Features: begins with a clear overview chapter to introduce this interdisciplinary field; discusses the classic network science of fixed connectivity structures, including empirical studies, mathematical models and computational algorithms; examines time-dependent processes that take place over networks, covering topics such as synchronisation, and message passing algorithms; investigates time-evolving networks, such as the World Wide Web and shifts in topological properties (connectivity, spectrum, percolation); explores applications of complex networks in the physical and engineering sciences, looking ahead to new developments in the field. Researchers and professionals from disciplines as varied as computer science, mathematics, engineering, physics, chemistry, biology, ecology, neuroscience, epidemiology, and the social sciences will all benefit from this topical and broad overview of current activities and grand challenges in the unfolding field of network science.
The emerging field of network science represents a new style of research that can unify such traditionally-diverse fields as sociology, economics, physics, biology, and computer science. It is a powerful tool in analyzing both natural and man-made systems, using the relationships between players within these networks and between the networks themselves to gain insight into the nature of each field. Until now, studies in network science have been focused on particular relationships that require varied and sometimes-incompatible datasets, which has kept it from being a truly universal discipline. Computational Network Science seeks to unify the methods used to analyze these diverse fields. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. This new approach would remove the need for tedious human-based analysis of different datasets and help researchers spend more time on the qualitative aspects of network science research. Demystifies media hype regarding Network Science and serves as a fast-paced introduction to state-of-the-art concepts and systems related to network science Comprehensive coverage of Network Science algorithms, methodologies, and common problems Includes references to formative and updated developments in the field Coverage spans mathematical sociology, economics, political science, and biological networks
In the 1980's, James Gleick's Chaos introduced the world to complexity. Now, Albert-László Barabási's Linked reveals the next major scientific leap: the study of networks. We've long suspected that we live in a small world, where everything is connected to everything else. Indeed, networks are pervasive--from the human brain to the Internet to the economy to our group of friends. These linkages, it turns out, aren't random. All networks, to the great surprise of scientists, have an underlying order and follow simple laws. Understanding the structure and behavior of these networks will help us do some amazing things, from designing the optimal organization of a firm to stopping a disease outbreak before it spreads catastrophically.In Linked, Barabási, a physicist whose work has revolutionized the study of networks, traces the development of this rapidly unfolding science and introduces us to the scientists carrying out this pioneering work. These "new cartographers" are mapping networks in a wide range of scientific disciplines, proving that social networks, corporations, and cells are more similar than they are different, and providing important new insights into the interconnected world around us. This knowledge, says Barabási, can shed light on the robustness of the Internet, the spread of fads and viruses, even the future of democracy. Engaging and authoritative, Linked provides an exciting preview of the next century in science, guaranteed to be transformed by these amazing discoveries.From Linked:This book has a simple message: think networks. It is about how networks emerge, what they look like, and how they evolve. It aims to develop a web-based view of nature, society, and technology, providing a unified framework to better understand issues ranging from the vulnerability of the Internet to the spread of diseases. Networks are present everywhere. All we need is an eye for them...We will see the challenges doctors face when they attempt to cure a disease by focusing on a single molecule or gene, disregarding the complex interconnected nature of the living matter. We will see that hackers are not alone in attacking networks: we all play Goliath, firing shots at a fragile ecological network that, without further support, could soon replicate our worst nightmares by turning us into an isolated group of species...Linked is meant to be an eye-opening trip that challenges you to walk across disciplines by stepping out of the box of reductionism. It is an invitation to explore link by link the next scientific revolution: the new science of networks.
Network Science and Cybersecurity introduces new research and development efforts for cybersecurity solutions and applications taking place within various U.S. Government Departments of Defense, industry and academic laboratories. This book examines new algorithms and tools, technology platforms and reconfigurable technologies for cybersecurity systems. Anomaly-based intrusion detection systems (IDS) are explored as a key component of any general network intrusion detection service, complementing signature-based IDS components by attempting to identify novel attacks. These attacks may not yet be known or have well-developed signatures. Methods are also suggested to simplify the construction of metrics in such a manner that they retain their ability to effectively cluster data, while simultaneously easing human interpretation of outliers. This is a professional book for practitioners or government employees working in cybersecurity, and can also be used as a reference. Advanced-level students in computer science or electrical engineering studying security will also find this book useful .
Committee on Network Science for Future Army Applications,Board on Army Science and Technology,Division on Engineering and Physical Sciences,National Research Council
Author: Committee on Network Science for Future Army Applications,Board on Army Science and Technology,Division on Engineering and Physical Sciences,National Research Council
Publisher: National Academies Press
The military is currently attempting to develop itself into a force capable of networkcentric operations. While this effort has highlighted the militaryâ€™s dependence on interacting networks, it has also shown that there is a huge gap between what we need to know about networks and our fundamental knowledge about network behavior. This gap makes the military vision of NCO problematic. To help address this problem, the Army asked the NRC to find out whether identifying and funding a new field of â€œnetwork scienceâ€ could help close this gap. This report presents an examination of networks andthe military, an analysis of the promise, content, and challenges of network science, and an assessment of approaches to creating value from network science.
Author: Alex Fornito,Andrew Zalesky,Edward Bullmore
Publisher: Academic Press
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
Alan Feldman,Cliff Konold,Bob Coulter,Brian Conroy
Author: Alan Feldman,Cliff Konold,Bob Coulter,Brian Conroy
Network Science, A Decade Later--the result of NSF-funded research that looked at the experiences of a set of science projects which use the Internet--offers an understanding of how the Internet can be used effectively by science teachers and students to support inquiry-based teaching and learning. The book emphasizes theoretical and critical perspectives and is intended to raise questions about the goals of education and the ways that technology helps reach those goals and ways that it cannot. The theoretical perspective of inquiry-based teaching and learning in which the book is grounded is consistent with the current discipline-based curriculum standards and frameworks. The chapters in Part I, "State of the Art," describe the history and current practice of network science. Those in Part II, "Looking Deeply," extend the inquiry into network science by examining discourse and data in depth, using both empirical data and theoretical perspectives. In Part III, "Looking Forward," the authors step back from the issues of network science to take a broader view, focusing on the question: How should the Internet be used--and not used--to support student learning? The book concludes with a reminder that technology will not replace teachers. Rather, the power of new technologies to give students both an overwhelming access to resources--experts, peers, teachers, texts, images, and data--and the opportunity to pursue questions of their own design, increases the need for highly skilled teachers and forward-looking administrators. This is a book for them, and for all educators, policymakers, students involved in science and technology education. For more information about the authors, an archived discussions space, a few chapters that can be downloaded as PDF files, and ordering information, visit teaparty.terc.edu/book/
"This book is structured into sections that look at some of the challenges related to coalition operations in different types of networks, such as communications and information networks and human and cognitive networks, and looks at other issues that impact the operations of coalitions, the management and use of policies across different organizations"--Provided by publisher.
Modern and Interdisciplinary Problems in Network Science: A Translational Research Perspective covers a broad range of concepts and methods, with a strong emphasis on interdisciplinarity. The topics range from analyzing mathematical properties of network-based methods to applying them to application areas. By covering this broad range of topics, the book aims to fill a gap in the contemporary literature in disciplines such as physics, applied mathematics and information sciences.
Mark Newman,Albert-László Barabási,Duncan J. Watts
Author: Mark Newman,Albert-László Barabási,Duncan J. Watts
Publisher: Princeton University Press
From the Internet to networks of friendship, disease transmission, and even terrorism, the concept--and the reality--of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields--including mathematics, physics, computer science, sociology, and biology--have been pursuing these questions and building a new "science of networks." This book brings together for the first time a set of seminal articles representing research from across these disciplines. It is an ideal sourcebook for the key research in this fast-growing field. The book is organized into four sections, each preceded by an editors' introduction summarizing its contents and general theme. The first section sets the stage by discussing some of the historical antecedents of contemporary research in the area. From there the book moves to the empirical side of the science of networks before turning to the foundational modeling ideas that have been the focus of much subsequent activity. The book closes by taking the reader to the cutting edge of network science--the relationship between network structure and system dynamics. From network robustness to the spread of disease, this section offers a potpourri of topics on this rapidly expanding frontier of the new science.
While there are sporadic journal articles on socio-technical networks, there’s long been a need for an integrated resource that addresses concrete socio-technical network (STN) design issues from algorithmic and engineering perspectives. Filling this need, Socio-Technical Networks: Science and Engineering Design provides a complete introduction to the fundamentals of one of the hottest research areas across the social sciences, networking, and computer science—including its definition, historical background, and models. Covering basic STN architecture from a physical/technological perspective, the book considers the system design process in a typical STN, including inputs, processes/actions, and outputs/products. It covers current applications, including transportation networks, energy systems, tele-healthcare, financial networks, and the World Wide Web. A group of STN expert contributors addresses privacy and security topics in the interdependent context of critical infrastructure, which include risk models, trust models, and privacy preserving schemes. Covers the physical and technological designs in a typical STN Considers STN applications in popular fields, such as healthcare and the virtual community Details a method for mapping and measuring complexity, uncertainty, and interactions among STN components The book examines the most important STN models, including graph theory, inferring agent dynamics, decision theory, and information mining. It also explains structural studies, behavioral studies, and agent/actor system studies and policy studies in different STN contexts. Complete with in-depth case studies, this book supplies the practical insight needed to address contemporary STN design issues.
Studying brain networks has become a truly interdisciplinary endeavor, attracting students and seasoned researchers alike from a wide variety of academic backgrounds. What has been lacking is an introductory textbook that brings together the different fields and provides a gentle introduction to the major concepts and findings in the emerging field of network neuroscience. Network Neuroscience is a one-stop-shop that is of equal use to the neurobiologist, who is interested in understanding the quantitative methods employed in network neuroscience, and to the physicist or engineer, who is interested in neuroscience applications of mathematical and engineering tools. The book spans 27 chapters that cover everything from individual cells all the way to complex network disorders such as depression and autism spectrum disorders. An additional 12 toolboxes provide the necessary background for making network neuroscience accessible independent of the reader’s background. Dr. Flavio Frohlich (www.networkneuroscientist.org) wrote this book based on his experience of mentoring dozens of trainees in the Frohlich Lab, from undergraduate students to senior researchers. The Frohlich lab (www.frohlichlab.org) pursues a unique and integrated vision that combines computer simulations, animal model studies, human studies, and clinical trials with the goal of developing novel brain stimulation treatments for psychiatric disorders. The book is based on a course he teaches at UNC that has attracted trainees from many different departments, including neuroscience, biomedical engineering, psychology, cell biology, physiology, neurology, and psychiatry. Dr. Frohlich has consistently received rave reviews for his teaching. With this book he hopes to make his integrated view of neuroscience available to trainees and researchers on a global scale. His goal is to make the book the training manual for the next generation of (network) neuroscientists, who will be fusing biology, engineering, and medicine to unravel the big questions about the brain and to revolutionize psychiatry and neurology. Easy-to-read, comprehensive introduction to the emerging field of network neuroscience Includes 27 chapters packed with information on topics from single neurons to complex network disorders such as depression and autism Features 12 toolboxes serve as primers to provide essential background knowledge in the fields of biology, mathematics, engineering, and physics
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.
Using social media and peer-to-peer networks to teach people about science and health may seem like an obvious strategy. Yet recent research suggests that systematic reliance on social networks may be a recipe for inequity. People are not consistently inclined to share information with others around them, and many people are constrained by factors outside of their immediate control. Ironically, the highly social nature of humankind complicates the extent to which we can live in a society united solely by electronic media. Stretching well beyond social media, this book documents disparate tendencies in the ways people learn and share information about health and science. By reviewing a wide array of existing research—ranging from a survey of New Orleans residents in the weeks after Hurricane Katrina to analysis of Twitter posts related to H1N1 to a physician-led communication campaign explaining the benefits of vaginal birth—Brian Southwell explains why some types of information are more likely to be shared than others and how some people never get exposed to seemingly widely available information. This book will appeal to social science students and citizens interested in the role of social networks in information diffusion and yet it also serves as a cautionary tale for communication practitioners and policymakers interested in leveraging social ties as an inexpensive method to spread information. -- K. "Vish" Viswanath, Harvard School of Public Health and the Dana-Farber Cancer Institute
This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to definition and implementation of the most important network models. The text provides information on the structure of the data and on the quality of available datasets. Furthermore it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose the authors have set up a dedicated web site where readers can download and test the codes. The whole project is aimed as a learning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks.
This invaluable book captures the proceedings of a workshop that brought together a group of distinguished scientists from a variety of disciplines to discuss how networking influences decision making. The individual lectures interconnect psychological testing, the modeling of neuron networks and brain dynamics to the transport of information within and between complex networks. Of particular importance was the introduction of a new principle that governs how complex networks talk to one another — the Principle of Complexity Management (PCM). PCM establishes that the transfer of information from a stimulating complex network to a responding complex network is determined by how the complexity indices of the two networks are related. The response runs the gamut from being independent of the perturbation to being completely dominated by it, depending on the complexity mismatch. Contents:Overview of ARO Program on Network Science for Human Decision Making (B J West)Viewing the Extended Mind Hypothesis(Clark & Chambers) in Terms of Complex Systems Dynamics (G Werner)Uncertainty in Psychophysics: Deriving a Network of Psychophysical Equations (K H Norwich)The Collective Brain (E Tagliazucchi and D R Chialvo)Acquiring Long-Range Memory Through Adaptive Avalanches (S Boettcher)Random Walk of Complex Networks: From Infinitely Slow to Instantaneous Transition to Equilibrium (N W Hollingshad, P Grigolini and P Allegrini)Coherence and Complexity (M Bologna, E Geneston, P Grigolini, M Turalska and M Lukovic)Quakes in Complex Systems as a Signature of Cooperation (E Geneston and P Grigolini)Renewal Processes in the Critical Brain (P Allegrini, P Paradisi, D Menicucci and A Gemignani)The Principle of Complexity Management (B J West and P Grigolini) Readership: For professional and research level studies in psychophysics. Keywords:Network Science;Complexity;Neural Avalanches;Brain Dynamics;Decision Making;Coherence;UncertaintyKey Features:Due to the heterogeneous nature of the audience the lectures avoid being overly specialized but maintain scientific integrity while reaching across disciplines thereby making them accessible to students at all levelsExplicit contact is made between the qualitative concepts of classical psychology such as the mind and the quantitative methods of psychophysics and neurophysiology by some of today's outstanding scientistsNo other treatment of decision making takes advantage of the technical developments that have been made in the analyses of complex networks over the past decade
This book contains original research chapters related to the interdisciplinary field of complex networks spanning biological and environmental networks, social, technological, and economic networks. Many natural phenomena can be modeled as networks where nodes are the primitive compounds and links represent their interactions, similarities, or distances of sorts. Complex networks have an enormous impact on research in various fields like biology, social sciences, engineering, and cyber-security to name a few. The topology of a network often encompasses important information on the functionality and dynamics of the system or the phenomenon it represents. Network science is an emerging interdisciplinary discipline that provides tools and insights to researchers in a variety of domains. NetSci-X is the central winter conference within the field and brings together leading researchers and innovators to connect, meet, and establish interdisciplinary channels for collaboration. It is the largest and best known event in the area of network science. This text demonstrates how ideas formulated by authors with different backgrounds are transformed into models, methods, and algorithms that are used to study complex systems across different domains and will appeal to researchers and students within in the field.
In network design, the gap between theory and practice is woefully broad. This book narrows it, comprehensively and critically examining current network design models and methods. You will learn where mathematical modeling and algorithmic optimization have been under-utilized. At the opposite extreme, you will learn where they tend to fail to contribute to the twin goals of network efficiency and cost-savings. Most of all, you will learn precisely how to tailor theoretical models to make them as useful as possible in practice. Throughout, the authors focus on the traffic demands encountered in the real world of network design. Their generic approach, however, allows problem formulations and solutions to be applied across the board to virtually any type of backbone communication or computer network. For beginners, this book is an excellent introduction. For seasoned professionals, it provides immediate solutions and a strong foundation for further advances in the use of mathematical modeling for network design. Written by leading researchers with a combined 40 years of industrial and academic network design experience. Considers the development of design models for different technologies, including TCP/IP, IDN, MPLS, ATM, SONET/SDH, and WDM. Discusses recent topics such as shortest path routing and fair bandwidth assignment in IP/MPLS networks. Addresses proper multi-layer modeling across network layers using different technologies—for example, IP over ATM over SONET, IP over WDM, and IDN over SONET. Covers restoration-oriented design methods that allow recovery from failures of large-capacity transport links and transit nodes. Presents, at the end of each chapter, exercises useful to both students and practitioners.