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Multi-hop Attention Graph Neural Network
[article]
2021
arXiv
pre-print
Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes. However, such attention mechanism does not account for nodes that are not directly connected but provide important network context. Here we propose Multi-hop Attention Graph Neural Network (MAGNA), a principled way to
arXiv:2009.14332v5
fatcat:vbtttkitzzdezllnx5chtxn3su