Spectral density estimation under local graph access
Given local random-neighbor access to an unknown unweighted graph with vertices, let denote its normalized adjacency matrix and let be its spectral density. Determine the query complexity, as a function of , of producing an estimator such that, with constant success probability, . In particular, establish whether every such classical estimator requires local queries, matching the known upper bound.
References
Primary source
Additional references
Progress summary
A new preprint claims the conjectured exponential number of local questions is necessary, matching the best known method, but its proof has not been independently verified.
The problem asks whether estimating a graph’s eigenvalue distribution from local random-neighbor access necessarily requires exponentially many queries as the accuracy improves.
Known results
- Cohen-Steiner, Kong, Sohler, and Valiant, 2018: a classical estimator in the random-neighbor model.
- Jin, Musco, Sidford, and Singh, 2023: a nearly matching lower bound for weighted graphs.
- Peng, 2026: for unweighted graphs and non-adaptive random-walk transcripts, ; the adaptive local-access case was left open.
August 2026 claimed resolution
The preprint Classical and quantum spectral density estimation under local graph access claims that every randomized classical estimator requires at least queries for sufficiently small , with constant success probability. Together with the known upper bound, this would establish complexity; the claim is unrefereed and unverified.
Current status (as of August 2026): The classical question is claimed resolved at , but the new lower-bound proof remains unverified.
Sources
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