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pith:2025:MPLQWPYXA76SM64RW6B2DLACKL
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Distinguishing finite metric spaces via similarity spectra

Jun O'Hara

The q-spectrum from similarity matrices distinguishes all finite metric spaces on at most four points and a large class of larger ones.

arxiv:2502.08980 v6 · 2025-02-13 · math.MG

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Claims

C1strongest claim

The q-spectrum completely distinguishes a large class of finite metric spaces and all metric spaces on at most 4 points. The transition q-spectrum distinguishes spaces for which the multiset of pairwise distances is independent over the rational numbers, along with all spaces on at most 3 points.

C2weakest assumption

The similarity matrices are constructed so that their spectra encode enough metric information to separate the claimed classes; the paper invokes this construction without an independent verification that the matrix definition is the minimal or canonical choice that preserves distinction power.

C3one line summary

Introduces q-spectrum and transition q-spectrum invariants for finite metric spaces that recover graph spectra in a limit and distinguish all spaces with at most 4 and 3 points respectively under stated conditions.

Receipt and verification
First computed 2026-06-02T01:03:30.516901Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

63d70b3f1707fd267b91b783a1ac0252fcdc263976e59587dd8ce2afe0df7381

Aliases

arxiv: 2502.08980 · arxiv_version: 2502.08980v6 · doi: 10.48550/arxiv.2502.08980 · pith_short_12: MPLQWPYXA76S · pith_short_16: MPLQWPYXA76SM64R · pith_short_8: MPLQWPYX
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/MPLQWPYXA76SM64RW6B2DLACKL \
  | 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: 63d70b3f1707fd267b91b783a1ac0252fcdc263976e59587dd8ce2afe0df7381
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "math.MG",
    "submitted_at": "2025-02-13T05:33:41Z",
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