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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 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.
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This content will become publicly available on March 31, 2026
What is the Value of Data? A Theory and Systematization
Data powers economies, shapes societies, and fuels decision-making, yet its value remains poorly understood. Despite its centrality, we lack a unified framework for defining, measuring, and reasoning about data’s worth. This article develops a theory and systematization of the value of data—explaining why, how, and when data generates value. We distinguish data from documents, separate objective value from subjective judgments, and identify key dimensions of data’s worth. Our framework reconciles disparate notions of information, knowledge, and utility, offering insights that validate known principles while uncovering new opportunities to extract value from data. More than a taxonomy, this work provides a conceptual foundation for integrating perspectives from computer science, economics, and beyond. The conceptual foundation clarifies data’s role in technology, markets, and governance, advancing our ability to systematically understand and harness its value.
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- Award ID(s):
- 2340034
- PAR ID:
- 10618456
- Publisher / Repository:
- ACM Digital Library
- Date Published:
- Journal Name:
- ACM / IMS Journal of Data Science
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2831-3194
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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