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Service platforms have moved into the center of interest in both academic research and the IT industry due to their economic and technical impact. These multitenant platforms provide own or third party software as metered, on-demand services. Corresponding service offers exhibit network effects. The present work introduces a graphical modeling language to support service platform design with focus on the exploitation of these network effects.
Das Interesse an der Netzwerkanalyse nimmt rapide zu. Bisher fehlt es jedoch an empirisch orientierten Einführungen. Das interdisziplinäre Autorenteam führt daher praxisorientiert in die Grundlagen und Methoden der empirischen Analyse sozialer Netzwerke ein. Schritt für Schritt wird der Forschungsprozess von der Untersuchungsplanung über die Auswertungsmethodik bis zur Präsentation der Ergebnisse erläutert. Damit ist das Lehrbuch für den Einsatz in Lehre, Forschung und Praxis geeignet. This textbook provides an introduction to the process of empirical network research. In an action-oriented approach, it features explicated learning goals, numerous reference examples, and exercises that facilitate successful learning. Integrating their different disciplinary perspectives, the authors address an interdisciplinary audience of teachers, researchers, and practitioners alike.
For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network.
The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
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.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce.