Advances in Graph Neural Networks

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Éditeur :

Springer

Paru le : 2022-11-16

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus...
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À propos


Éditeur

Collection
n.c

Parution
2022-11-16

Pages
198 pages

EAN papier
9783031161735

Auteur(s) du livre


Chuan Shi, PhD., is a Professor and Deputy Director of Beijing Key Lab of Intelligent Telecommunications Software and Multimedia at the Beijing University of Posts and Telecommunications.  He received his B.S. from Jilin University in 2001, his M.S. from Wuhan University in 2004, and his Ph.D. from the ICT of Chinese Academic of Sciences in 2007.  His research interests include data mining, machine learning, and evolutionary computing. He has published more than 100 papers in refereed journals and conferences.Xiao Wang, Ph.D., is an Associate Professor in the School of Computer Science at the Beijing University of Posts and Telecommunications. He received his Ph.D. from the School of Computer Science and Technology at Tianjin University in 2016. He was a postdoctoral researcher in the Department of Computer Science and Technology at Tsinghua University.  His current research interests include data mining, social network analysis, and machine learning. He has published more than 70 papers in refereed journals and conferences.Cheng Yang, Ph.D., is an Associate Professor at the Beijing University of Posts and Telecommunications. He received his B.E. and Ph.D. from Tsinghua University in 2014 and 2019, respectively. His research interests include natural language processing and network representation learning. He has published more than 20 top-level papers in international journals and conferences including ACM TOIS, EMNLP, IJCAI, and AAAI.

Caractéristiques détaillées - droits

EAN PDF
9783031161742
Prix
63,29 €
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
5673 Ko
EAN EPUB
9783031161742
Prix
63,29 €
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
26317 Ko

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