arXiv:2605.18623v2 Announce Type: replace-cross Abstract: In the graph label selection problem, one is given an $n$-vertex graph and a budget $k$, and seeks to select $k$ vertices whose labels enable accurate prediction of the labels on the remaining vertices. This problem formalizes distilling a small representative set from the whole graph. We present the first $\tilde{O}(\log^{1.5} n)$-approximation algorithm for graph label selection under the standard budget constraint. Prior work either relies on resource augmentation, allowing substantially more than $k$ labeled vertices, or consists pr
This research provides a more efficient algorithm for a fundamental problem in graph-based machine learning, improving upon prior methods that were either less accurate or resource-intensive.
Improved graph label selection algorithms can significantly enhance the efficiency and accuracy of AI models that rely on contextual data relationships, impacting various AI applications.
The ability to distill representative data from large graphs with $\tilde{O}(\log^{1.5} n)$-approximation marks a step forward in data efficiency and model interpretability in AI.
- · AI researchers
- · Machine learning platform providers
- · Industries relying on graph-based insights
More efficient and accurate graph-based AI models can be developed and deployed.
This could lead to advancements in areas like recommendation systems, social network analysis, and drug discovery by optimizing data selection.
Further scaling of AI applications that grapple with large, unlabeled datasets could accelerate, impacting the overall efficiency of AI systems.
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Read at arXiv cs.LG