Shi Heterogeneous Graph Representation Learning and Applications 9789811661686

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Автор: Shi
Название: Heterogeneous Graph Representation Learning and Applications
ISBN: 9789811661686
Издательство: Springer
Классификация:

Базы данных

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Экспертные системы / системы баз знаний

Машинное обучение

ISBN-10: 9811661685
Обложка/Формат: Soft cover
Страницы: 318
Вес: 0.52 кг.
Дата издания: 15.02.2023
Серия: Artificial Intelligence: Foundations, Theory, and Algorithms
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 40 tables, color; 1 illustrations, black and white; xx, 318 p. 1 illus.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
Дополнительное описание: Introduction.- The State-of-the-art of Heterogeneous Graph Representation.- Part One: Techniques.- Structure-preserved Heterogeneous Graph Representation.- Attribute-assisted Heterogeneous Graph Representation.- Dynamic Heterogeneous Graph Representation.

Heterogeneous Graph Representation Learning and Applications, Shi