An Approximation Algorithm for Graph Label Selection
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 primarily of heuristics without provable guarantees. Finally, we demonstrate that practical heuristic variants of our algorithm scale to significantly larger...
| Authors | Josia John, Simon Meierhans, Maximilian Probst Gutenberg |
|---|---|
| Primary category | cs.DS |
| Published | 18 May 2026 |
| Last updated at source | 19 May 2026 |
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