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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Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - PP(2025) vom: 08. Mai
1. Verfasser:	Chen, Zhixian (VerfasserIn)
Weitere Verfasser:	Ma, Tengfei, Song, Yangqiu, Wang, Yang
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2025
Zugriff auf das übergeordnete Werk:	IEEE transactions on pattern analysis and machine intelligence
Schlagworte:	Journal Article