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DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network.

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1 Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3185527

Abstract: Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among which graph convolutional networks (GCNs) have emerged… read more here.

Keywords: graph convolutional; dynamic graph; graph; efficient dynamic ... See more keywords