QUEST方法改进了不确定知识图谱补全，通过谱初始化和图正则化提升了预测准确性和训练稳定性。
QUEST方法引入谱初始化和图平滑调度，在不确定知识图谱(UKG)补全任务中取得突破。该方法使用置信加权图拉普拉斯矩阵的最小非平凡特征向量初始化实体嵌入，并在训练早期应用无偏小批量狄利克雷能量正则化。在两个UKG数据集上，QUEST在八组指标数据对中的六组上超越先前方法，其余两组与最佳方法持平，同时消除了密集图上的不稳定波动。
Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph Completion
Uncertain knowledge graphs (UKGs) extend knowledge graphs by assigning each triple a continuous confidence score. Since most possible triples lack observed confidences, recent methods rely on semi-supervised learning to generate pseudo-labels. These methods initialize entity embeddings without using the confidence-weighted graph, discarding its global community and hub structure. We introduce QUEST, which adds no trainable parameters to the standard confidence-distribution learning pipeline. First, QUEST initializes entity embeddings using the smallest non-trivial eigenvectors of the confidence-weighted graph Laplacian, incorporating community and hub structure before training. Second, QUEST applies an unbiased mini-batch Dirichlet energy regularizer to enforce early-stage structural consistency. On two UKG datasets, QUEST improves confidence prediction and link prediction on six of eight metric-dataset pairs over prior methods and matches the previous best on the remaining two, while removing the instability spike observed on dense graphs. These results indicate that spectral structural priors combined with a graph Dirichlet energy regularizer improve accuracy, training stability, and checkpoint reliability in UKG completion.