.png){kind=icon}
返回
Dual-decoder graph autoencoder for unsupervised graph representation learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.107564.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Graph clustering
Graph autoencoder
Graph representation learning
Graph neural networks
Graph embedding
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests
OAAI
OAAI分享收藏Oscillatory activity is not evident in the primate temporal visual cortex with static stimuli.png)
NeuroReport
NeuroReportIF0
PREAI
分享收藏
The changing epidemiology of paediatric brain tumours: a review from the Hospital for Sick Children
PREAI
分享收藏A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications图嵌入的综合调查: 问题，技术和应用OAAI
OAAI分享收藏
查看更多内容