Summary of Precedence-constrained Winter Value For Effective Graph Data Valuation, by Hongliang Chi et al.
Precedence-Constrained Winter Value for Effective Graph Data Valuation
by Hongliang Chi, Wei Jin, Charu Aggarwal, Yao Ma
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce an innovative solution called Precedence-Constrained Winter (PC-Winter) Value for graph data valuation. Existing methods are effective for Euclidean data but struggle with graph-structured data due to its intricate dependencies and exponential growth in value estimation costs. The proposed approach accounts for the complex graph structure, and strategies are developed to address computational challenges. Extensive experiments demonstrate the effectiveness of PC-Winter across diverse datasets and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to measure how valuable different pieces of data are. It’s important because it helps us figure out if we’re getting fair value for our data, and whether it’s good quality or not. The problem is that most methods only work well with one type of data called Euclidean, but there’s more types like graph-structured data that need special handling. |
