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Jie Zhou

Introduction to Graph Neural Networks

Buch

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding m… Mehr

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Produktdetails


Weitere Autoren: Liu, Zhiyuan
  • ISBN: 978-3-031-00459-9
  • EAN: 9783031004599
  • Produktnummer: 39048187
  • Verlag: Springer International Publishing
  • Sprache: Englisch
  • Erscheinungsjahr: 2020
  • Seitenangabe: 128 S.
  • Masse: H23.5 cm x B19.1 cm x D0.7 cm 255 g
  • Abbildungen: Paperback
  • Gewicht: 255

Über den Autor


Zhiyuan Liu is an associate professor in the Department of Computer Science and Technology, Tsinghua University. He got his B.E. in 2006 and his Ph.D. in 2011 from the Department of Computer Science and Technology, Tsinghua University. His research interests are natural language processing and social computation. He has published over 60 papers in international journals and conferences, including IJCAI, AAAI, ACL, and EMNLP.Jie Zhou is a second-year Masters student of the Department of Computer Science and Technology, Tsinghua University. He got his B.E. from Tsinghua University in 2016. His research interests include graph neural networks and natural language processing.

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