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This is an overview and structured analysis of contemporary multilayer network visualization. It surveys techniques as well as tools, tasks, and analytics from within application domains. It also identifies research opportunities and examines outstanding challenges along with potential solutions and future research directions for addressing them. Visual Analysis of Multilayer Networks is not only for visualization researchers, but for those who need to visualize multilayer networks in the domain of complex systems, as well as anyone solving problems within application domains. The emergence of multilayer networks as a concept from the field of complex systems provides many new opportunities for the visualization of network complexity, and has also raised many new exciting challenges. The multilayer network model recognizes that the complexity of relationships between entities in real-world systems is better embraced as several interdependent subsystems (or layers) rather than a simple graph approach. Despite only recently being formalized and defined, this model can be applied to problems in the domains of life sciences, sociology, digital humanities, and more. Within the domain of network visualization there already are many existing systems, which visualize data sets having many characteristics of multilayer networks, and many techniques, which are applicable to their visualization.
The emergence of multilayer networks as a concept from the field of complex systems provides many new opportunities for the visualization of network complexity, and has also raised many new exciting challenges. The multilayer network model recognizes that the complexity of relationships between entities in real-world systems is better embraced as several interdependent subsystems (or layers) rather than a simple graph approach. Despite only recently being formalized and defined, this model can be applied to problems in the domains of life sciences, sociology, digital humanities, and more. Within the domain of network visualization there already are many existing systems, which visualize data sets having many characteristics of multilayer networks, and many techniques, which are applicable to their visualization. In this Synthesis Lecture, we provide an overview and structured analysis of contemporary multilayer network visualization. This is not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those solving problems within application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer network visualization, as well as tools, tasks, and analytic techniques from within application domains. We also identify the research opportunities and examine outstanding challenges for multilayer network visualization along with potential solutions and future research directions for addressing them.
The adoption of multilayer analysis techniques is rapidly expanding across all areas of knowledge, from social sciences (the first facing the complexity of such structures, decades ago) to computer science, from biology to engineering. However, until now, no book has dealt exclusively with the analysis and visualization of multilayer networks. Multilayer Networks: Analysis and Visualization provides a guided introduction to one of the most complete computational frameworks, named muxViz, with introductory information about the underlying theoretical aspects and a focus on the analytical side. Dozens of analytical scripts and examples to use the muxViz library in practice, by means of the Graphical User Interface or by means of the R scripting language, are provided. In addition to researchers in the field of network science, as well as practitioners interested in network visualization and analysis, this book will appeal to researchers without strong technical or computer science background who want to learn how to use muxViz software, such as researchers from humanities, social science and biology: audiences which are targeted by case studies included in the book. Other interdisciplinary audiences include computer science, physics, neuroscience, genetics, urban transport and engineering, digital humanities, social and computational social science. Readers will learn how to use, in a very practical way (i.e., without focusing on theoretical aspects), the algorithms developed by the community and implemented in the free and open-source software muxViz. The data used in the book is available on a dedicated (open and free) site.
This book provides the basis of a formal language and explores its possibilities in the characterization of multiplex networks. Armed with the formalism developed, the authors define structural metrics for multiplex networks. A methodology to generalize monoplex structural metrics to multiplex networks is also presented so that the reader will be able to generalize other metrics of interest in a systematic way. Therefore, this book will serve as a guide for the theoretical development of new multiplex metrics. Furthermore, this Brief describes the spectral properties of these networks in relation to concepts from algebraic graph theory and the theory of matrix polynomials. The text is rounded off by analyzing the different structural transitions present in multiplex systems as well as by a brief overview of some representative dynamical processes. Multiplex Networks will appeal to students, researchers, and professionals within the fields of network science, graph theory, and data science.
This book unifies and consolidates methods for analyzing multilayer networks arising from the social and physical sciences and computing.
This book demonstrates the application of network theory to the social organization of animals.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.
This book reflects the outcome of contribution by the plural community and of the interactions between disciplines. With the mass of data available through Information and Communication Technologies (ICT) in an unprecedented quantity since the Human History, it is now possible to access dimensions of knowledge that, though not hidden, could not be grasped in the same way in the past. The question of how this information can be used for the benefit of institutional and economic actors to foster the development of a territory. Tackling the issue from a resolutely interdisciplinary perspective, the authors explore the theories and methods of complex systems in order to discuss how they can contribute in these new circumstances to territorial intelligence and to the development practices in which it is embodied. This book illustrates how today’s research explores the multiple facets of territorial systems in order to reproduce their richness. It invites readers to learn about the challenges, ideas, results and advances present in this domain.
Representation and analysis of networks are one of the fundamental ways to understand large, complex systems like brains, autonomous systems, and social networks. Such large systems are modeled as a network by representing their real-world objects as nodes and edges as the relationship between them. In neuroscience, for example, a core component of analysis involves modeling the connectivity of the brain into a network, where the nodes represent brain regions and the edges, the correlation among their activations. Visualization and statistical analysis of the resulting network data have provided tremendous value including identifying diagnostic indicators for neuro-degenerative diseases and age-related disruptions in the brain. Interactive visual analytic systems for exploring essential network properties have furthered our understanding of real-world systems. Many of the unforeseen patterns prevalent in brain networks are understood through the intuitive insights that these systems enable. However, existing methods for visual analytics primarily focus on static networks and do not satisfactorily explore the rich properties of network such as structural, hierarchical, semantic and time-variance. The comprehensive understanding of such properties and their complex dependencies is crucial to holistically inferring novel insights concerning the scientific data. This dissertation contributes novel visual analytics methods that explore such properties of multi-modal networks in neuroscience, autonomous systems, and social science. First, we focus on a visual analysis method that allows us to interactively explore the hierarchical modular structure of a fMRI-based brain network. Second, we contribute a multi-scale visual analysis approach to explore the time-varying modular structure of an ECoG-based brain network. Third, we contribute a novel visualization technique that identifies similarities and differences between dynamic graphs for intuitive analysis of temporal states in a time-varying network. Fourth, we propose a novel difference graph visual analysis technique that reveals significant time-varying patterns in the network dataset over multiple temporal scales. Further, the dissertation concludes with high-level ideas and techniques that are investigated and briefly explores open research areas in graph visualization.
Corporate networks, the links between companies and their leaders, reflect a country’s economic organization and its corporate governance system. Most research on corporate networks focuses on individual countries or particular time periods, however, making fruitful comparisons over longer periods of time difficult. This book provides a unique long-term analysis of the rise, consolidation, decline, and occasional re-emergence of these networks in fourteen countries across North and South America, Europe, and Asia in the 20th and early 21st centuries. In this volume, the editors bring together the most internationally well-known specialists to investigate the long-term development of corporate networks. Using a combination of quantitative and qualitative research approaches, the authors describe the main developments and changes in the corporate network over time by focusing on important network indicators in benchmark years, and identify historical explanations for these developments. This unique, long-term perspective allows readers insight into how and why national corporate networks have evolved over time.