Precedence-Constrained Winter Value for Effective Graph Data Valuation

Hongliang Chi, Wei Jin, Charu C. Aggarwal, Yao Ma

Keywords: Data Valuation, Graph Learning, Graph neural network

TL;DR: This paper proposes PC-Winter, a novel and effective method for valuating elements in graph-structured data, providing important new insights into the challenging problem of graph data valuation.

Abstract: 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.

Submission Number: 445