WalkGCN: a biased sampling strategy for GNNs on non-attributed graphs

Abstract Graph Neural Networks (GNNs) typically assume the presence of node attributes to capture interactions in a graph structure. However, real-world graph data often has incomplete or completely-missing attribute information. GNN approaches to dealing with incomplete attributes are widely implem...

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Hlavní autoři:	Mincheol Shin, Taeyoung Choe, Yejong Ryu, Yanggon Kim, Mucheol Kim
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	SpringerOpen 2025-09-01
Edice:	Journal of Big Data
Témata:	Graph embedding Attribute missing Graph neural networks Artificial attributes Random walk
On-line přístup:	https://doi.org/10.1186/s40537-025-01270-y
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