Before revision: To explore heterogeneous information networks (HINs), network representation learning (NRL) is proposed, which represents a network in a low-dimension space . Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue.
After revision: In network representation learning (NRL) is proposed, which represents a network in a low-dimension space . Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue.