Garantía de entrega
SKU/Artículo: AMZ-9811661650
Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms)
Format:
Hardcover
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.86 kg
0.86 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- 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.
U$S 302,04
55% OFF
U$S 137,29
IMPORTÁ FACIL
Comprando este producto podrás descontar el IVA con tu número de RUT
NO CONSUME FRANQUICIA
Si tu carrito tiene solo libros o CD’s, no consume franquicia y podés comprar hasta U$S 1000 al año.
U$S 302,04
55% OFF
U$S 137,29
¡Comprá en hasta 12 cuotas sin interés con todas tus tarjetas!
Llega en 25 o más días hábiles
con envío
Tienes garantía de entrega
Compra protegida
Disfruta de una experiencia de compra segura y confiable
![]()
Garantía de entrega
Con Tiendamia todas tus compras cuentan con Garantía de Entrega o devolución total de tu dinero.
Compras 100% seguras y garantizadas, para que pidas lo que soñás y lo recibas del mundo a tu puerta.
Saber más.
Tarjetas prepagas, debito, y credito
Paga hasta en 12 cuotas sin interés con tarjetas de crédito.
Visa
Mastercard
American Express
Dinners
Discover
Lider
Midinero
OCA
OCA Blue
Tarjeta PREX
Pagos a través de PayPal
Paga en dólares con Tarjetas internacionales a través de PayPal.
PayPal
