Heterogeneous graph representation learning and applications [electronic resource] / Chuan Shi, Xiao Wang, Philip S. Yu.

Unlimited

Authorized users

Can lend chapters, not whole ebooks

Heterogeneous graph representation learning and applications [electronic resource] / Chuan Shi, Xiao Wang, Philip S. Yu.

9789811661662 (electronic bk.)
9811661669 (electronic bk.)
9811661650
9789811661655

Singapore : Springer, 2021.

English

1 online resource.

10.1007/978-981-16-6166-2 doi

QA76.88

004/.35

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.

Includes bibliographical references.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed February 4, 2022).

Artificial intelligence: foundations, theory, and algorithms.

Print version: 9789811661655

Online Resources > Ebooks
All Resources