Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effective in evaluating the value of Euclidean data, they face limitations when applied to the increasingly popular graph-structured data. Particularly, graph data valuation introduces unique challenges, primarily stemming from the intricate dependencies among nodes and the exponential growth in value estimation costs. To address the challenging problem of graph data valuation, we put forth an innovative solution, Precedence-Constrained Winter (PC-Winter) Value, to account for the complex graph structure. Furthermore, we develop a variety of strategies to address the computational challenges and enable efficient approximation of PC-Winter. Extensive experiments demonstrate the effectiveness of PC-Winter across diverse datasets and tasks.

翻译：数据估值对于量化数据价值至关重要，有助于评估数据质量和确定公平补偿。尽管现有数据估值方法在评估欧几里得数据的价值方面已证明有效，但当应用于日益流行的图结构数据时，它们面临局限性。特别是图数据估值带来了独特的挑战，主要源于节点间复杂的依赖关系以及价值估计成本的指数级增长。为了解决图数据估值这一具有挑战性的问题，我们提出了一种创新解决方案——基于先行约束的Winter（PC-Winter）值，以考虑复杂的图结构。此外，我们开发了多种策略来应对计算挑战，并实现PC-Winter的高效近似。大量实验证明了PC-Winter在多种数据集和任务上的有效性。