pith:U237QWH5
Hyperbolic Latent Space Models for Network Embedding: Model Specification and Bayesian Inference
Inferring the temperature parameter in hyperbolic latent space models captures network tree-like topology better than fixing it.
arxiv:2605.11340 v2 · 2026-05-11 · stat.ME
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Claims
We demonstrate that temperature is the fundamental parameter governing a network's tree-like topology, and that failing to infer it weakens model expressiveness.
That the hyperbolic geometry with a temperature-modulated distance-to-probability mapping is the correct generative model for the hierarchical structure observed in the networks under study, and that the proposed inference procedures can reliably recover this temperature from finite data.
A Bayesian hyperbolic latent space model with inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.
Receipt and verification
| First computed | 2026-06-11T01:09:37.585465Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
a6b7f858fdeb50d0ec9c3de36830036ea9b0e9b5cf13bdfc9e6a6922da8c701d
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/U237QWH55NINB3E4HXRWQMADN2 \
| 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: a6b7f858fdeb50d0ec9c3de36830036ea9b0e9b5cf13bdfc9e6a6922da8c701d
Canonical record JSON
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