<ul><li>Knowledge Graph Embeddings (KGEs) have been developed to analyze KGs and predict new facts based on the information in a KG.</li><li>The Topologically-Weighted Intelligence Generation (TWIG) model is an extension of KGEs that can simulate the performance of KGE models on different hyperparameter settings and KGs.</li><li>TWIG can accurately predict hyperparameter performance on unseen KGs in the zero-shot setting, suggesting the potential for pre-hoc hyperparameter selection using TWIG-like methods.</li><li>Further research can explore the use of TWIG to determine optimal hyperparameter selection for KGE models without the need for a full hyperparameter search.</li></ul>

Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure

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