Precedence-Constrained Winter Value for Effective Graph Data Valuation
This addresses the need for fair compensation and quality assessment in graph data, but it is incremental as it adapts existing valuation methods to a new data type.
The paper tackles the problem of data valuation for graph-structured data, which is challenging due to node dependencies and high computational costs, by proposing the Precedence-Constrained Winter (PC-Winter) Value and efficient approximation strategies, showing effectiveness in experiments across diverse datasets and tasks.
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.