Graph neural networks: A review of methods and applications

Graph neural networks: A review of methods and applications

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learni...

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Bibliographic Details
Main Authors: Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2020-01-01
Series:AI Open
Subjects:
Deep learning
Graph neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S2666651021000012

Internet

http://www.sciencedirect.com/science/article/pii/S2666651021000012

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