NetMA averages latent space models with different dimensions using K-fold edge cross-validation, and is claimed to be asymptotically optimal for link prediction while outperforming true-model fitting when the true dimension is large.
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Network Model Averaging Prediction for Latent Space Models by K-Fold Edge Cross-Validation
NetMA averages latent space models with different dimensions using K-fold edge cross-validation, and is claimed to be asymptotically optimal for link prediction while outperforming true-model fitting when the true dimension is large.