Responsible Graph Neural Networks
Download Responsible Graph Neural Networks PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Responsible Graph Neural Networks book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Responsible Graph Neural Networks
More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications. Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details. Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.
Digital Healthcare, Digital Transformation and Citizen Empowerment in Asia-Pacific and Europe for a Healthier Society
Author: Patricia Ordonez de Pablos
language: en
Publisher: Elsevier
Release Date: 2025-01-09
Digital Healthcare, Digital Transformation and Citizen Empowerment in Asia-Pacific and Europe for a Healthier Society explores the potential of advanced IT in healthcare. This book shares insights on leveraging IT tools to address global disruptions like the pandemic, offering case studies from various regions, innovative topics in digital healthcare, lessons from the COVID-19 pandemic, and recommendations for policymakers worldwide. This title is a valuable resource for researchers, clinicians, CEOs, and policymakers seeking to learn from both failures and successes and harness the potential of advanced information technologies to enhance healthcare systems and services. - Shows the urgent need to understand the potential of advanced IT for the healthcare sector - Gives insight into the successes and failures during pandemics - Exploits the potential of AI, big data, and knowledge management to provide better healthcare services and more digital inclusion of citizen - Includes study cases in the field of IT and digital health during the pandemic and analyses lessons learned
Concepts and Techniques of Graph Neural Networks
Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.