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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.
| Autorius: | Chuan Shi, Philip S. Yu, Xiao Wang, |
| Serija: | Artificial Intelligence: Foundations, Theory, and Algorithms |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2023 |
| Knygos puslapių skaičius: | 340 |
| ISBN-10: | 9811661685 |
| ISBN-13: | 9789811661686 |
| Formatas: | 235 x 155 x 19 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Heterogeneous Graph Representation Learning and Applications“
