Pith. sign in

Reconstructing random graphs from distance queries

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
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.

fields

cs.CC 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Complexity of learning matchings and half graphs via edge queries

cs.CC · 2025-07-03 · conditional · novelty 6.0

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.

citing papers explorer

Showing 1 of 1 citing paper.

  • Complexity of learning matchings and half graphs via edge queries cs.CC · 2025-07-03 · conditional · none · ref 27 · internal anchor

    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.