Автор: Ma, Yao (michigan State University) Tang, Jiliang (michigan State University) Название: Deep learning on graphs ISBN: 1108831745 ISBN-13(EAN): 9781108831741 Издательство: Cambridge Academ Рейтинг: Цена: 7126.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, and scalability and then demonstrates applications in NLP, data mining, vision and healthcare.
Автор: Mordeson Название: Fuzzy Graph Theory with Applications to Human Trafficking ISBN: 3319764535 ISBN-13(EAN): 9783319764535 Издательство: Springer Рейтинг: Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reports on advanced concepts in fuzzy graph theory, showing a set of tools that can be successfully applied to understanding and modeling illegal human trafficking.
Автор: Mathew Название: Fuzzy Graph Theory ISBN: 3319714066 ISBN-13(EAN): 9783319714066 Издательство: Springer Рейтинг: Цена: 20733.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a timely overview of fuzzy graph theory, laying the foundation for future applications in a broad range of areas.
Автор: Satyanarayana, Bhavanari , Prasad, Kuncham Syam Название: Near Rings, Fuzzy Ideals, and Graph Theory ISBN: 0367380048 ISBN-13(EAN): 9780367380045 Издательство: Taylor&Francis Рейтинг: Цена: 10411.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Near Rings, Fuzzy Ideals, and Graph Theory explores the relationship between near rings and fuzzy sets and between near rings and graph theory. It covers topics from recent literature along with several characterizations.
After introducing all of the necessary fundamentals of algebraic systems, the book presents the essentials of near rings theory, relevant examples, notations, and simple theorems. It then describes the prime ideal concept in near rings, takes a rigorous approach to the dimension theory of N-groups, gives some detailed proofs of matrix near rings, and discusses the gamma near ring, which is a generalization of both gamma rings and near rings. The authors also provide an introduction to fuzzy algebraic systems, particularly the fuzzy ideals of near rings and gamma near rings. The final chapter explains important concepts in graph theory, including directed hypercubes, dimension, prime graphs, and graphs with respect to ideals in near rings.
Near ring theory has many applications in areas as diverse as digital computing, sequential mechanics, automata theory, graph theory, and combinatorics. Suitable for researchers and graduate students, this book provides readers with an understanding of near ring theory and its connection to fuzzy ideals and graph theory.
Автор: Liu Zhiyuan, Zhou Jie Название: Introduction to Graph Neural Networks ISBN: 1681737655 ISBN-13(EAN): 9781681737652 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 6376.00 р. Наличие на складе: Поставка под заказ.
Описание: Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks.
However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.
This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
Автор: Pal Madhumangal, Samanta Sovan, Ghorai Ganesh Название: Modern Trends in Fuzzy Graph Theory ISBN: 9811588023 ISBN-13(EAN): 9789811588020 Издательство: Springer Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the world of mathematics and computer science, technological advancements are constantly being researched and applied to ongoing issues. Setbacks in social networking, engineering, and automation are themes that affect everyday life, and researchers have been looking for new techniques in which to solve these challenges. Graph theory is a widely studied topic that is now being applied to real-life problems.
Advanced Applications of Graph Theory in Modern Society is an essential reference source that discusses recent developments on graph theory, as well as its representation in social networks, artificial neural networks, and many complex networks. The book aims to study results that are useful in the fields of robotics and machine learning and will examine different engineering issues that are closely related to fuzzy graph theory. Featuring research on topics such as artificial neural systems and robotics, this book is ideally designed for mathematicians, research scholars, practitioners, professionals, engineers, and students seeking an innovative overview of graphic theory.
Автор: Sunil Mathew; John N. Mordeson; Davender S. Malik Название: Fuzzy Graph Theory ISBN: 3319890700 ISBN-13(EAN): 9783319890708 Издательство: Springer Рейтинг: Цена: 20733.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a timely overview of fuzzy graph theory, laying the foundation for future applications in a broad range of areas. It introduces readers to fundamental theories, such as Craine’s work on fuzzy interval graphs, fuzzy analogs of Marczewski’s theorem, and the Gilmore and Hoffman characterization. It also introduces them to the Fulkerson and Gross characterization and Menger’s theorem, the applications of which will be discussed in a forthcoming book by the same authors. This book also discusses in detail important concepts such as connectivity, distance and saturation in fuzzy graphs. Thanks to the good balance between the basics of fuzzy graph theory and new findings obtained by the authors, the book offers an excellent reference guide for advanced undergraduate and graduate students in mathematics, engineering and computer science, and an inspiring read for all researchers interested in new developments in fuzzy logic and applied mathematics.
Автор: John N. Mordeson; Sunil Mathew Название: Advanced Topics in Fuzzy Graph Theory ISBN: 3030042146 ISBN-13(EAN): 9783030042141 Издательство: Springer Рейтинг: Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book builds on two recently published books by the same authors on fuzzy graph theory. Continuing in their tradition, it provides readers with an extensive set of tools for applying fuzzy mathematics and graph theory to social problems such as human trafficking and illegal immigration. Further, it especially focuses on advanced concepts such as connectivity and Wiener indices in fuzzy graphs, distance, operations on fuzzy graphs involving t-norms, and the application of dialectic synthesis in fuzzy graph theory. Each chapter also discusses a number of key, representative applications. Given its approach, the book provides readers with an authoritative, self-contained guide to – and at the same time an inspiring read on – the theory and modern applications of fuzzy graphs. For newcomers, the book also includes a brief introduction to fuzzy sets, fuzzy relations and fuzzy graphs.
Автор: Liu Zhiyuan, Zhou Jie Название: Introduction to Graph Neural Networks ISBN: 1681737671 ISBN-13(EAN): 9781681737676 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 9286.00 р. Наличие на складе: Поставка под заказ.
Описание: Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks.
However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.
This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
Автор: John N. Mordeson; Sunil Mathew; Davender S. Malik Название: Fuzzy Graph Theory with Applications to Human Trafficking ISBN: 3030094944 ISBN-13(EAN): 9783030094942 Издательство: Springer Рейтинг: Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reports on advanced concepts in fuzzy graph theory, showing a set of tools that can be successfully applied to understanding and modeling illegal human trafficking. Building on the previous book on fuzzy graph by the same authors, which set the fundamentals for readers to understand this developing field of research, this second book gives a special emphasis to applications of the theory. For this, authors introduce new concepts, such as intuitionistic fuzzy graphs, the concept of independence and domination in fuzzy graphs, as well as directed fuzzy networks, incidence graphs and many more.
Автор: Pal Madhumangal, Samanta Sovan, Ghorai Ganesh Название: Modern Trends in Fuzzy Graph Theory ISBN: 9811588058 ISBN-13(EAN): 9789811588051 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
