A perturbation-recovery generative autoencoder for heterogeneous graphs with attributes missing
Article
Article
Abstract Heterogeneous graphs are widely employed in applications such as social networks, recommendation systems, and bioinformatics. However, node attributes in real-world heterogeneous graphs are often missing or corr...
LCC LCC:Medicine; TENDOlNjaWVuY2U~Idioma English
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