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University of Southern California
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Incorporating spectral structural priors into uncertain knowledge graph completion can dramatically enhance prediction accuracy and training stability without adding extra parameters.
Identical embeddings for non-isomorphic graphs reveal the limitations of global-attention graph transformers in capturing the nuanced structure of mixed-integer linear programs.
