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Node-private community estimation in stochastic block models: Tractable algorithms and lower bounds

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arXiv:2605. 15943v1 Announce Type: cross Abstract: We study the classical problem of community recovery in stochastic block models with a fixed number of communities, with a twist: We seek algorithms that are stable with respect to node-wise changes in the graph structure, formally defined as a differential privacy constraint.

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Authors: Laurentiu Marchis, Ethan D'souza, Tom\'a\v{s} Fl\'idr, Po-Ling Loh
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Node-private community estimation in stochastic block models: Tractable algorithms and lower bounds overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, class, rect, under, line, rate, project, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.15943

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