Cubic spectral conjecture for random geometric graph detection
Let be a random geometric graph generated by independent uniform points on a high-dimensional sphere, with connection function applied to endpoint inner products, and let be the Erdős–Rényi graph with the same edge density. If is the centered and standardized spherical kernel operator, then the conjecture asserts that
Equivalently, for every sequence of tests distinguishing the two graph models, the sum of the two error probabilities has lower limit at least .
References
Primary source
Additional references
Progress summary
A September 2026 preprint claims to refute the conjecture for unrestricted connection functions, while restricted versions remain open.
The conjecture proposes that detection is governed by the cubic spectral quantity , with detectability above and impossibility below the threshold. A February 2026 paper presented this as a conjecture, not a theorem.
Known results
- For a fixed smooth kernel with , the critical dimension is , and signed triangles detect below this threshold.
- For a quadratic kernel, the reported test threshold is .
- For hard random geometric graphs, a July 2026 paper proves impossibility for with , covering the conjectured threshold when ; the regime remains open.
September 10, 2026 counterexample
Congyi Luo’s preprint claims that the cubic-trace condition fails for dimension-dependent, nonmonotone connection functions: cubic statistics can cancel while fourth-order statistics retain signal. The claim targets the formulation without monotonicity or a common-sign spectral condition and has not been independently verified.
Current status (as of September 2026): The unrestricted conjecture is claimed refuted by an unverified counterexample; versions with monotonicity or common-sign spectral assumptions, and the hard-kernel regime , remain open.
Solutions 0
No solutions have been posted yet.