Summary of Disentangled Structural and Featural Representation For Task-agnostic Graph Valuation, by Ali Falahati et al.
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation
by Ali Falahati, Mohammad Mohammadi Amiri
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (stat.ML)
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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 This paper proposes a novel framework called blind message passing to evaluate the value of graph-structured data without relying on specific task-related metrics. The framework aligns the buyer’s and seller’s graphs using a shared node permutation based on graph matching, allowing for quantification of differences in structural distribution through the graph Wasserstein distance. Additionally, featural aspects are considered to capture statistical similarities and differences between buyers’ and sellers’ graphs, referred to as relevance and diversity respectively. The approach ensures that buyers and sellers remain unaware of each other’s datasets. Experiments on real datasets demonstrate the effectiveness of this framework in capturing relevance, diversity, and structural disparities of seller data for buyers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out how valuable some data is without knowing what it’s used for. This paper helps with that by breaking down complex graphs into smaller parts and comparing them to each other. The researchers developed a new way to match these graph structures, allowing them to measure how different they are. They also looked at the characteristics of the graphs themselves, like how similar or different they are. This approach is useful because it doesn’t require knowing what the data is used for, making it practical in real-world scenarios where validation might not be possible. |
