ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction

The paper introduces ImbalancE, an inference‑time latent search method that mitigates degree imbalance bias in Knowledge Graph Embedding models. It targets the problematic prediction of target entities with much lower degrees than anchor entities, a common issue in recommender systems and other applications. Experiments on benchmark datasets show that ImbalancE improves predictions on the most imbalanced triples compared to conventional methods.

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• rag
• benchmarks
• safety