Graph Neural Networks Essentials And Use Cases

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Graph Neural Networks Essentials And Use Cases

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Author : Pethuru Raj Chelliah
language : en
Publisher: Springer
Release Date : 2025-08-27

Graph Neural Networks Essentials And Use Cases written by Pethuru Raj Chelliah and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-27 with Computers categories.

This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals—and how they complement advances in generative, agentic, and physical AI—this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data—complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node’s local neighborhood by aggregating information from adjacent nodes, updating the node’s representation in the process. Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance. Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.

Graph Neural Networks Essentials And Use Cases

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Author : Pethuru Raj Chelliah
language : en
Publisher: Springer Nature
Release Date : 2025-07-25

Graph Neural Networks Essentials And Use Cases written by Pethuru Raj Chelliah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.

This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals—and how they complement advances in generative, agentic, and physical AI—this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data—complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node’s local neighborhood by aggregating information from adjacent nodes, updating the node’s representation in the process. Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance. Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.

Responsible Graph Neural Networks

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Author : Mohamed Abdel-Basset
language : en
Publisher: CRC Press
Release Date : 2023-06-05

Responsible Graph Neural Networks written by Mohamed Abdel-Basset and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-05 with Computers categories.

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.

The Essentials Of Machine Learning And Neural Networks

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Author : Dr.R.Gopinath
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-07-18

The Essentials Of Machine Learning And Neural Networks written by Dr.R.Gopinath and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-18 with Computers categories.

Dr.R.Gopinath, Associate Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India. Mr.R.Vijay Sai, Assistant Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India. Mr.P.Sathishkumar, Associate Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India. Dr.A.Gnanabaskaran, Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India.

Pytorch Essentials A Comprehensive Guide To Machine Learning Techniques

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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2024-12-03

Pytorch Essentials A Comprehensive Guide To Machine Learning Techniques written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.

"PyTorch Essentials: A Comprehensive Guide to Machine Learning Techniques" is an essential resource for programmers, researchers, and enthusiasts eager to enhance their mastery of machine learning and artificial intelligence through the PyTorch framework. This guide deftly combines theoretical foundations with hands-on applications, covering everything from basic tensor manipulation and neural network creation to sophisticated architectures such as CNNs, RNNs, and generative models. Additionally, it delves into reinforcement learning, optimization strategies, and best practices for deployment, empowering readers with the skills to develop scalable, production-ready solutions. Whether advancing your research or solving real-world industry challenges, this book serves as your indispensable guide to mastering machine learning with PyTorch.

Dataflow Supercomputing Essentials

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Author : Veljko Milutinovic
language : en
Publisher: Springer
Release Date : 2017-12-11

Dataflow Supercomputing Essentials written by Veljko Milutinovic and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-11 with Computers categories.

This illuminating text/reference reviews the fundamentals of programming for effective DataFlow computing. The DataFlow paradigm enables considerable increases in speed and reductions in power consumption for supercomputing processes, yet the programming model requires a distinctly different approach. The algorithms and examples showcased in this book will help the reader to develop their understanding of the advantages and unique features of this methodology. This work serves as a companion title to DataFlow Supercomputing Essentials: Research, Development and Education, which analyzes the latest research in this area, and the training resources available. Topics and features: presents an implementation of Neural Networks using the DataFlow paradigm, as an alternative to the traditional ControlFlow approach; discusses a solution to the three-dimensional Poisson equation, using the Fourier method and DataFlow technology; examines how the performance of the Binary Search algorithm can be improved through implementation on a DataFlow architecture; reviews the different way of thinking required to best configure the DataFlow engines for the processing of data in space flowing through the devices; highlights how the DataFlow approach can efficiently support applications in big data analytics, deep learning, and the Internet of Things. This indispensable volume will benefit all researchers interested in supercomputing in general, and DataFlow computing in particular. Advanced undergraduate and graduate students involved in courses on Data Mining, Microprocessor Systems, and VLSI Systems, will also find the book to be an invaluable resource.

Graph Representation Learning

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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

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.

Essentials Of Cognitive Neuroscience

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Author : Bradley R. Postle
language : en
Publisher: John Wiley & Sons
Release Date : 2020-05-27

Essentials Of Cognitive Neuroscience written by Bradley R. Postle and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-27 with Psychology categories.

Essentials of Cognitive Neuroscience introduces and explicates key principles and concepts in cognitive neuroscience in such a way that the reader will be equipped to critically evaluate the ever-growing body of findings that the field is generating. For some students this knowledge will be needed for subsequent formal study, and for all readers it will be needed to evaluate and interpret reports about cognitive neuroscience research that make their way daily into the news media and popular culture. The book seeks to do so in a style that will give the student a sense of what it's like to be a cognitive neuroscientist: when confronted with a problem, how does one proceed? How does one read and interpret research that's outside of one's sub-area of specialization? How do two scientists advancing mutually incompatible models interrelate? Most importantly, what does it feel like to partake in the wonder and excitement of this most dynamic and fundamental of sciences?

Digital Logic Design Exam Essentials

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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date : 2024-10-26

Digital Logic Design Exam Essentials written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Study Aids categories.

Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com

Neural Network Fundamentals With Graphs Algorithms And Applications

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Author : Nirmal K. Bose
language : en
Publisher: McGraw-Hill Companies
Release Date : 1996

Neural Network Fundamentals With Graphs Algorithms And Applications written by Nirmal K. Bose and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.