标题： 评估累积谱梯度作为复杂性度量 显示英文标题

标题： Evaluating Cumulative Spectral Gradient as a Complexity Measure

摘要： 准确估计数据集复杂性对于评估和比较知识图谱（KGs）的链接预测模型至关重要。 累积谱梯度（CSG）指标是从谱聚类框架内类之间的概率发散中推导出的，被提出作为数据集复杂性的度量，它（1）随着类数自然扩展，（2）与下游分类性能有很强的相关性。 在本工作中，我们严格评估了CSG在标准知识图谱链接预测基准和多类尾部预测任务上的行为，使用两个控制其计算的关键参数，M，每个类的蒙特卡洛采样点数，和K，嵌入空间中的最近邻数。 与原始声明相反，我们发现（1）CSG对K的选择非常敏感，因此不会随着目标类数而自动扩展，（2）CSG值与均倒数排名（MRR）等现有性能指标的相关性很弱或没有相关性。 通过在FB15k 237、WN18RR和其他标准数据集上的实验，我们证明了CSG所声称的稳定性和泛化预测能力在链接预测设置中失效。 我们的结果突显了在KG链接预测评估中需要更稳健、与分类器无关的复杂性度量。 显示英文摘要

摘要： Accurate estimation of dataset complexity is crucial for evaluating and comparing link prediction models for knowledge graphs (KGs). The Cumulative Spectral Gradient (CSG) metric derived from probabilistic divergence between classes within a spectral clustering framework was proposed as a dataset complexity measure that (1) naturally scales with the number of classes and (2) correlates strongly with downstream classification performance. In this work, we rigorously assess CSG behavior on standard knowledge graph link prediction benchmarks a multi class tail prediction task, using two key parameters governing its computation, M, the number of Monte Carlo sampled points per class, and K, the number of nearest neighbors in the embedding space. Contrary to the original claims, we find that (1) CSG is highly sensitive to the choice of K and therefore does not inherently scale with the number of target classes, and (2) CSG values exhibit weak or no correlation with established performance metrics such as mean reciprocal rank (MRR). Through experiments on FB15k 237, WN18RR, and other standard datasets, we demonstrate that CSG purported stability and generalization predictive power break down in link prediction settings. Our results highlight the need for more robust, classifier agnostic complexity measures in KG link prediction evaluation.