AEGCN: An Autoencoder-Constrained Graph Convolutional Network

We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convolutional network operating directly on graphs, whose hidden layers are constraine...

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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 432; pp. 21 - 31
Main Authors:	Ma, Mingyuan, Na, Sen, Wang, Hongyu
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 07.04.2021
Subjects:	Graph autoencoder Graph convolutional networks Graph node classification Homogeneous and heterogeneous graphs Graph autoencoder Graph node classification Graph convolutional networks Homogeneous and heterogeneous graphs
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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