The spectral weak-recovery threshold for linearized AMP in the multi-view spiked Wigner model is SNR(λ,B)=1, where SNR is the largest eigenvalue of Diag(√λ)(B⊙B)Diag(√λ), and this coincides with the information-theoretic threshold for a broad class of spike priors.
General community detection with op- timal recovery conditions for multi-relational sparse networks with dependent layers
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A model-independent framework converts mild low-degree testing advantages into conditional computational lower bounds for recovery tasks, recovering prior results for planted submatrix and SBM while providing new evidence for detection-recovery gaps in angular synchronization and multi-layer models.
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Sharp Spectral Thresholds for Multi-View Spiked Wigner Models
The spectral weak-recovery threshold for linearized AMP in the multi-view spiked Wigner model is SNR(λ,B)=1, where SNR is the largest eigenvalue of Diag(√λ)(B⊙B)Diag(√λ), and this coincides with the information-theoretic threshold for a broad class of spike priors.
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Algorithmic Contiguity from Low-Degree Heuristic II: Predicting Detection-Recovery Gaps
A model-independent framework converts mild low-degree testing advantages into conditional computational lower bounds for recovery tasks, recovering prior results for planted submatrix and SBM while providing new evidence for detection-recovery gaps in angular synchronization and multi-layer models.