  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.
