Dual-decoder graph autoencoder for unsupervised graph representation learning

Unsupervised graph representation learning is a challenging task that embeds graph data into a low-dimensional space without label guidance. Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks. However, most existing graph autoencode...

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Vydané v:Knowledge-based systems Ročník 234; s. 107564
Hlavní autori: Sun, Dengdi, Li, Dashuang, Ding, Zhuanlian, Zhang, Xingyi, Tang, Jin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 25.12.2021
Elsevier Science Ltd
Predmet:
Affinity
Algorithms
Attributes
Clustering
Decoders
Embedding
Feature maps
Graph autoencoder
Graph clustering
Graph embedding
Graph neural networks
Graph representation learning
Graph representations
Graphical representations
Learning
Machine learning
Representation
Graph clustering
Graph representation learning
Graph autoencoder
Graph neural networks
Graph embedding
ISSN:0950-7051, 1872-7409
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Popis
Shrnutí:Unsupervised graph representation learning is a challenging task that embeds graph data into a low-dimensional space without label guidance. Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks. However, most existing graph autoencoder-based embedding algorithms only reconstruct the feature maps of nodes or the affinity matrix but do not fully leverage the latent information encoded in the low-dimensional representation. In this study, we propose a dual-decoder graph autoencoder model for attributed graph embedding. The proposed framework embeds the graph topological structure and node attributes into a compact representation, and then the two decoders are trained to reconstruct the node attributes and graph structures simultaneously. The experimental results on clustering and link prediction tasks strongly support the conclusion that the proposed model outperforms the state-of-the-art approaches.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.107564