A Dilated Recurrent Neural Network-Based Model for Graph Embedding
Graph embedding aims to preserve graphs into low-dimensional embedding space while preserving their properties. As thus, the embeddings can be easily exploited by downstream graph-based tasks. Most existing models of graph embedding strive to preserve some proximity properties of graphs such as the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8666969/ |