A perturbation-recovery generative autoencoder for heterogeneous graphs with attributes missing
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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 eng
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