mlsanityData to 7 Oct 2026

Precedence-Constrained Winter Value for Effective Graph Data Valuation

arXiv 2402.01943 ↗First posted 2 Feb 2024cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

26 of 44 references.

References · 26

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14 more
  1. 2019Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithmscited by 147
  2. 2022Data Banzhaf: A Robust Data Valuation Framework for Machine Learningcited by 126
  3. 2019Data Valuation using Reinforcement Learningcited by 115
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  6. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  7. 2020A Survey on Data Pricing: from Economics to Data Sciencecited by 39
  8. 2023Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Valuecited by 38
  9. 2023Data Valuation Without Training of a Modelcited by 35
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  12. 2022Characterizing the Influence of Graph Elementscited by 16
  13. 2023Item Graph Convolution Collaborative Filtering for Inductive Recommendationscited by 2
  14. 2022Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutionscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuationcited by 2
  2. 2024LossVal: Efficient Data Valuation for Neural Networkscited by 1
  3. 2025Unifying and Optimizing Data Values for Selection via Sequential Decision-Makingcited by 1
  4. 2025Shapley-Guided Utility Learning for Effective Graph Inference Data Valuationcited by 1
  5. 2024Explaining Graph Neural Networks for Node Similarity on Graphscited by 0
  6. 2026Efficient Decentralized Multi-task Dataset Valuation via Model Mergingcited by 0