Near-optimal node-private community estimation in polynomial-time

Laurentiu Marchis, Olga Klopp, Po-Ling Loh, Ilias Zadik

Under the stated balanced stochastic-block-model signal and community-growth assumptions, the authors give a high-probability polynomial-time pure node-private estimator attaining the prior exponential-time method’s minimax exact-recovery risk, with privacy parameter of order log n.

Sources for this record

What was already known

Klopp and Zadik had a minimax-rate node-private exact-recovery method with exponential runtime. Other computationally feasible node-private methods addressed weaker consistent recovery. The paper explicitly frames the polynomial-time minimax-rate combination as the open question.

AI involvement AI-centered

The authors credit ChatGPT 5.5 Plus with the initial polynomial-time algorithm idea and proof outlines. They corrected and completed the analysis and explain the final estimator.

The declaration identifies ChatGPT's initial polynomial-time algorithm idea as critical; the authors completed and corrected the proof.

Importance Notable

Closes a stated computational gap for private exact recovery under the paper’s assumptions.

Limitations

A truncated variant trades a larger privacy parameter for worst-case polynomial runtime. The authors corrected and rigorized the model’s initial idea, but no separately completed theorem-specific check is documented.

Technical verification Reported

The paper contains a full proof but documents no completed independent or author checking procedure for the final theorem. Reported is the evidence default, not a failed check.

Human understanding Full

The human authors describe the central score-surrogate and certified rejection-sampling mechanism and its analysis.

Correction and publication history

2026-10-18 · publication

ChatGPT supplied the initial polynomial-time algorithm idea; the authors completed its analysis.

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