Wasserstein Graph Neural Networks for Graphs with Missing Attributes

Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks' representation learning. Existing GNNs often struggle to effectively leverage incomplete attribute information, as they are not specifically designed for graphs with missing a...

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Publié dans:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - PP(2025) vom: 08. Mai
Auteur principal:	Chen, Zhixian (Auteur)
Autres auteurs:	Ma, Tengfei, Song, Yangqiu, Wang, Yang
Format:	Article en ligne
Langue:	English
Publié:	2025
Accès à la collection:	IEEE transactions on pattern analysis and machine intelligence
Sujets:	Journal Article