IGCN : A Provably Informative GCN Embedding for Semi-Supervised Learning With Extremely Limited Labels
Graph Neural Networks (GNNs) have gained much more attention in the representation learning for the graph-structured data. However, the labels are always limited in the graph, which easily leads to the overfitting problem and causes the poor performance. To solve this problem, we propose a new frame...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 12 vom: 23. Dez., Seite 8396-8409
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| 1. Verfasser: |
Zhang, Lin
(VerfasserIn) |
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| Weitere Verfasser: |
Song, Ran,
Tan, Wenhao,
Ma, Lin,
Zhang, Wei |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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