Learning compact graph representations via an encoder-decoder network

Learning compact graph representations via an encoder-decoder network

Abstract Feature representation learning for classification of multiple graphs is a problem with practical applications in many domains. For instance, in chemoinformatics, the learned feature representations of molecular graphs can be used to classify molecules which exhibit anti-cancer properties....

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Bibliographic Details
Main Authors: John Boaz Lee, Xiangnan Kong
Format: Article
Language:English
Published: SpringerOpen 2019-07-01
Series:Applied Network Science
Subjects:
Deep learning
Encoder-decoder
Graph classification
Graph representation
RNN
Online Access:http://link.springer.com/article/10.1007/s41109-019-0157-9

Internet

http://link.springer.com/article/10.1007/s41109-019-0157-9

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