Tight edge-query bounds are proven for learning matchings (deterministic n(n-1)/2, randomized Θ(n^2)) and half graphs (Θ(n log n) classically for column-permuted, Θ(n log n) quantum in general), with half-graph learning reduced to sorting problems.
Reconstructing random graphs from distance queries
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We estimate the minimum number of distance queries that is sufficient to reconstruct the binomial random graph $G(n,p)$ with constant diameter with high probability. We get a tight (up to a constant factor) answer for all $p>n^{-1+o(1)}$ outside "threshold windows" around $n^{-k/(k+1)+o(1)}$, $k\in\mathbb{Z}_{>0}$: with high probability the query complexity equals $\Theta(n^{4-d}p^{2-d})$, where $d$ is the diameter of the random graph. This demonstrates the following non-monotone behaviour: the query complexity jumps down at moments when the diameter gets larger; yet, between these moments the query complexity grows. We also show that there exists a non-adaptive algorithm that reconstructs the random graph with $O(n^{4-d}p^{2-d}\ln n)$ distance queries with high probability, and this is best possible.
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cs.CC 1years
2025 1verdicts
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Complexity of learning matchings and half graphs via edge queries
Tight edge-query bounds are proven for learning matchings (deterministic n(n-1)/2, randomized Θ(n^2)) and half graphs (Θ(n log n) classically for column-permuted, Θ(n log n) quantum in general), with half-graph learning reduced to sorting problems.