Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph Completion

The paper introduces QUEST, a method for uncertain knowledge graph completion that adds no trainable parameters to the standard pipeline. QUEST first initializes entity embeddings using the smallest non‑trivial eigenvectors of the confidence‑weighted graph Laplacian, thereby preserving community and hub structure before training. It then applies an unbiased mini‑batch Dirichlet energy regularizer to enforce early‑stage structural consistency, leading to improved confidence and link prediction on most metric‑dataset pairs and eliminating instability spikes on dense graphs.

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