Pith Number

pith:NDTNYNFJ

pith:2026:NDTNYNFJIGVPS5WLTEF54KOXQ3

not attested not anchored not stored refs resolved

HYVINT: Intensity-Driven Hypergraph Generation with Variational Representations

Shuntuo Xu, Xinyi Hong, Zhou Yu

HYVINT generates hypergraphs by linking latent interaction strengths to binary incidences with an intensity-driven mechanism.

arxiv:2605.16836 v1 · 2026-05-16 · stat.ML · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{NDTNYNFJIGVPS5WLTEF54KOXQ3}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

HYVINT achieves strong fidelity while maintaining substantial novelty and diversity on synthetic and real-world hypergraphs, supported by generation error bounds with asymptotic convergence rates.

C2weakest assumption

That linking latent interaction strength to binary incidence via an intensity-driven mechanism supplies a meaningfully more interpretable and accurate model of heterogeneous higher-order interactions than prior implicit latent spaces or continuous decoders.

C3one line summary

HYVINT introduces an intensity-driven incidence mechanism and tractable variational estimator for hypergraph generation, with error bounds and empirical gains in fidelity, novelty, and diversity.

References

53 extracted · 53 resolved · 2 Pith anchors

[1] Learning with hypergraphs: Clustering, classification, and embedding.Advances in neural information processing systems, 19, 2006 2006

[2] Molecular hypergraph grammar with its application to molecular optimization 2019

[3] Evolutionary dynamics of higher-order interactions in social networks.Nature Human Behaviour, 5(5): 586–595, 2021 2021

[4] Hypergraph contrastive col- laborative filtering 2022

[5] scmhnn: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data.Briefings in Bioinformatics, 24(6):bbad391, 2023 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:25.356037Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

68e6dc34a941aaf976cb990bde29d786e6bff012d1ea909bab41ffedd67da4b8

Aliases

arxiv: 2605.16836 · arxiv_version: 2605.16836v1 · doi: 10.48550/arxiv.2605.16836 · pith_short_12: NDTNYNFJIGVP · pith_short_16: NDTNYNFJIGVPS5WL · pith_short_8: NDTNYNFJ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NDTNYNFJIGVPS5WLTEF54KOXQ3 \
  | 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: 68e6dc34a941aaf976cb990bde29d786e6bff012d1ea909bab41ffedd67da4b8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2f76268368513445656f55c32b0b365465c0c72b856413e8a716f5cd47f4ac7c",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-16T06:38:33Z",
    "title_canon_sha256": "02cfcee772304b5209c8df2b85e733b2bd3e56461d3313254814fbd434fa27d5"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16836",
    "kind": "arxiv",
    "version": 1
  }
}