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...

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Publié dans:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 12 vom: 23. Dez., Seite 8396-8409
Auteur principal:	Zhang, Lin (Auteur)
Autres auteurs:	Song, Ran, Tan, Wenhao, Ma, Lin, Zhang, Wei
Format:	Article en ligne
Langue:	English
Publié:	2024
Accès à la collection:	IEEE transactions on pattern analysis and machine intelligence
Sujets:	Journal Article