2023
DOI: 10.1016/j.jksuci.2023.101855
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Multi-view learning-based heterogeneous network representation learning

Lei Chen,
Yuan Li,
Xingye Deng
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Cited by 1 publication
(1 citation statement)
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“…Quintuple [22] introduces a quintuple-based learning model for bipartite heterogeneous networks, focusing on sophisticated representation by capturing complex interactions between diverse node types, significantly enhancing tasks like classification and prediction within these networks. Multi [23] presents a novel multi-view learning approach for representation learning in heterogeneous networks, effectively integrating diverse data types and views to enhance node representation, which significantly benefits tasks like node classification and link prediction. Hinormer [24] leverages a graph transformer to perform node representation learning in heterogeneous information networks, enhancing the capture of structural and semantic node information, which improves performance on various network analysis tasks.…”
Section: Related Workmentioning
confidence: 99%
“…Quintuple [22] introduces a quintuple-based learning model for bipartite heterogeneous networks, focusing on sophisticated representation by capturing complex interactions between diverse node types, significantly enhancing tasks like classification and prediction within these networks. Multi [23] presents a novel multi-view learning approach for representation learning in heterogeneous networks, effectively integrating diverse data types and views to enhance node representation, which significantly benefits tasks like node classification and link prediction. Hinormer [24] leverages a graph transformer to perform node representation learning in heterogeneous information networks, enhancing the capture of structural and semantic node information, which improves performance on various network analysis tasks.…”
Section: Related Workmentioning
confidence: 99%