pith:PYVDSWJ6
The feasibility of multi-graph alignment: a Bayesian approach
Above a critical threshold exact multi-graph alignment is achievable with high probability in the Gaussian model
arxiv:2502.17142 v4 · 2025-02-24 · math.ST · math.PR · stat.ML · stat.TH
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Claims
In the Gaussian model, above a critical threshold, exact alignment is achievable with high probability, while below it, even partial alignment is statistically impossible.
The random multi-graph generative models (Gaussian weights and sparse Erdős-Rényi edges) correctly capture the statistical setting, and the newly developed Bayesian estimation framework over metric spaces yields the precise information-theoretic feasibility thresholds without hidden modeling assumptions.
Establishes an all-or-nothing threshold for exact multi-graph alignment in the Gaussian model and a partial-alignment threshold in the sparse Erdős-Rényi model using a general Bayesian estimation framework over metric spaces.
Receipt and verification
| First computed | 2026-05-25T02:01:02.437895Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7e2a39593e1dc8600ace7e725bb1fd9f2363950bc12d4f76472d8a4980dff43a
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/PYVDSWJ6DXEGACWOPZZFXMP5T4 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7e2a39593e1dc8600ace7e725bb1fd9f2363950bc12d4f76472d8a4980dff43a
Canonical record JSON
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