HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings | Paper Citation Record

Observation f9c5556b-7039-43be-b0e1-2a3958f496dd · outbound

This paper cites Learning with hypergraphs: Clustering, classification, and embedding.Advances in neural information processing systems, 19, 2006.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Learning with hypergraphs: Clustering, classification, and embedding.Advances in neural information processing systems, 19, 2006

Reference 1

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Observation a71201b1-e09b-45ee-a2d7-704328ef204c · outbound

This paper cites Molecular hypergraph grammar with its application to molecular optimization.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Molecular hypergraph grammar with its application to molecular optimization

Reference 2

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Observation 923d506b-1459-484f-8efa-e23352f5b722 · outbound

This paper cites Evolutionary dynamics of higher-order interactions in social networks.Nature Human Behaviour, 5(5): 586–595, 2021.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Evolutionary dynamics of higher-order interactions in social networks.Nature Human Behaviour, 5(5): 586–595, 2021

Reference 3

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Observation 3fc665a9-776e-40ed-88e2-96c8ce1f64b8 · outbound

This paper cites Hypergraph contrastive col- laborative filtering.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Hypergraph contrastive col- laborative filtering

Reference 4

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Observation a0782d7e-806a-4a04-960f-78c6366d7721 · outbound

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

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings scmhnn: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data.Briefings in Bioinformatics, 24(6):bbad391, 2023

Reference 5

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Observation 8559dc77-f745-4317-9949-4bb321f1e423 · outbound

This paper cites Link prediction in social networks using hyper-motif representation on hypergraph.Multimedia Systems, 30(3):123, 2024.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Link prediction in social networks using hyper-motif representation on hypergraph.Multimedia Systems, 30(3):123, 2024

Reference 6

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Observation 66e17468-fba5-470f-8ef7-bb2989bf5eca · outbound

This paper cites A hypergraph neural network for prioritizing alzheimer’s disease risk genes.Frontiers in Genetics, 16:1668200, 2025.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings A hypergraph neural network for prioritizing alzheimer’s disease risk genes.Frontiers in Genetics, 16:1668200, 2025

Reference 7

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Observation 8083980a-ab47-4398-acd4-cfdcaac0a597 · outbound

This paper cites A survey on hypergraph representation learning.ACM Computing Surveys, 56(1):1–38, 2023.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings A survey on hypergraph representation learning.ACM Computing Surveys, 56(1):1–38, 2023

Reference 8

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Observation 61096a74-c6ab-4ad7-ab68-4b4cf2f0fcbb · outbound

This paper cites Hypergraph theory.An introduction.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Hypergraph theory.An introduction

Reference 9

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Observation 981337d0-225e-45ac-8518-01e5696f9875 · outbound

This paper cites Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes.Nature communications, 14(1):1605, 2023.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes.Nature communications, 14(1):1605, 2023

Reference 10

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Observation d24789d6-276b-4aad-9252-0ecec755aa56 · outbound

This paper cites Partial recovery and weak consistency in the non-uniform hypergraph stochastic block model.Combinatorics, Probability and Computing, 34(1):1–51, 2025.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Partial recovery and weak consistency in the non-uniform hypergraph stochastic block model.Combinatorics, Probability and Computing, 34(1):1–51, 2025

Reference 11

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Observation 18cd07c5-b1aa-4f08-b152-cd84ae1f3e4e · outbound

This paper cites A survey on hypergraph neural networks: an in-depth and step-by-step guide.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings A survey on hypergraph neural networks: an in-depth and step-by-step guide

Reference 12

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Observation a26a3234-cce0-4103-98b5-73ab5c88b12b · outbound

This paper cites A survey on hypergraph mining: Patterns, tools, and generators.ACM Computing Surveys, 57(8):1–36, 2025.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings A survey on hypergraph mining: Patterns, tools, and generators.ACM Computing Surveys, 57(8):1–36, 2025

Reference 13

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Observation c8bac5d8-db89-4af0-ad65-885b774a78bc · outbound

This paper cites Characterization multimodal connectivity of brain network by hypergraph gan for alzheimer’s disease analysis.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Characterization multimodal connectivity of brain network by hypergraph gan for alzheimer’s disease analysis

Reference 14

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Observation 4908821f-f5d5-48a3-8ad2-2de855b69e47 · outbound

This paper cites Multimodal representa- tions learning and adversarial hypergraph fusion for early alzheimer’s disease prediction.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Multimodal representa- tions learning and adversarial hypergraph fusion for early alzheimer’s disease prediction

Reference 15

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Observation 49ce9b8c-dd36-4222-b089-99e255e9f2ff · outbound

This paper cites Hypergraph structure learning for hypergraph neural networks.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Hypergraph structure learning for hypergraph neural networks

Reference 16

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Observation 940b3910-6c41-4fee-83e3-e37b375d46e7 · outbound

This paper cites Generating real-world hypergraphs via deep generative models.Information Sciences, 647:119412, 2023.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Generating real-world hypergraphs via deep generative models.Information Sciences, 647:119412, 2023

Reference 17

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Observation a2f663f9-42f8-4bd6-8c06-ba027d8ef5fa · outbound

This paper cites Hyperplr: Hypergraph generation through projection, learning, and reconstruction.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Hyperplr: Hypergraph generation through projection, learning, and reconstruction

Reference 18

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Observation 31333abf-44ff-4764-a1aa-7f7b398d4d74 · outbound

This paper cites Hygene: A diffusion-based hypergraph generation method.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Hygene: A diffusion-based hypergraph generation method

Reference 19

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Observation 5bafe2d9-57e8-49f0-9355-50eebe5ba34c · outbound

This paper cites Denoising Diffused Embeddings: a Generative Approach for Hypergraphs.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Denoising Diffused Embeddings: a Generative Approach for Hypergraphs

Reference 20

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Observation aae34699-1e25-46a3-98a2-f23655a1710a · outbound

This paper cites LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation

Reference 21

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Observation ead35093-9bbb-4e4b-882e-152eccfea50e · outbound

This paper cites Modeling hypergraph using large language models.arXiv preprint arXiv:2510.11728, 2025.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Modeling hypergraph using large language models.arXiv preprint arXiv:2510.11728, 2025

Reference 22

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Observation b0ef0584-70e8-41b2-9356-812910c3ae67 · outbound

This paper cites A systematic survey on deep generative models for graph generation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5370–5390, 2022.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings A systematic survey on deep generative models for graph generation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5370–5390, 2022

Reference 23

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Observation 6b49e492-e518-4bf4-883f-e5ae07845e19 · outbound

This paper cites Recent advances in hypergraph neural networks: M.-r.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Recent advances in hypergraph neural networks: M.-r

Reference 24

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Observation 000422f7-e7f3-40ce-b9e9-4e6db5cd4e24 · outbound

This paper cites A general latent embedding approach for modeling non-uniform high- dimensional sparse hypergraphs with multiplicity.arXiv preprint arXiv:2410.12108, 2024.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings A general latent embedding approach for modeling non-uniform high- dimensional sparse hypergraphs with multiplicity.arXiv preprint arXiv:2410.12108, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:47.550513Z

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Observation e025bafa-fb78-498f-89d9-59d8003d6f04 · outbound

This paper cites Evolution of real-world hypergraphs: Patterns and models without oracles.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Evolution of real-world hypergraphs: Patterns and models without oracles

Reference 26

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Observation 8ecab2f1-c8f3-43ed-a048-3c009e365ffc · outbound

This paper cites Effective and Efficient Attributed Hypergraph Embedding on Nodes and Hyperedges.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Effective and Efficient Attributed Hypergraph Embedding on Nodes and Hyperedges

Reference 27

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Observation 169ddfa1-be08-40fe-918d-8795090cfc3d · outbound

This paper cites Implicit hypergraph neural network.arXiv preprint arXiv:2508.14101, 2025.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Implicit hypergraph neural network.arXiv preprint arXiv:2508.14101, 2025

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:47.868317Z

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Observation ff81227d-ae55-489e-ab37-459d2dc5dab5 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Deep unsupervised learning using nonequilibrium thermodynamics

Reference 29

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Observation 33c153f1-2b4c-43ca-8791-7c3e7cab6e60 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 30

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Observation e6b84ba4-41f2-49c7-89b1-6f275d420d0e · outbound

This paper cites Improved denoising diffusion probabilistic models.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Improved denoising diffusion probabilistic models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:48.192599Z

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Observation 4e39ea46-25d0-4c04-a71c-6aee7d6eac0a · outbound

This paper cites Denoising Diffusion Implicit Models.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Denoising Diffusion Implicit Models

Reference 32

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Observation c93ac4b1-6600-462b-96ba-496c03251341 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

Reference 33

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Observation 01bb499c-6c1c-4c10-ba55-f78c12b34308 · outbound

This paper cites Relash: Reconstructing joint latent spaces for efficient generation of synthetic hypergraphs with hyperlink attributes.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Relash: Reconstructing joint latent spaces for efficient generation of synthetic hypergraphs with hyperlink attributes

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:48.530817Z

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Observation bfff0d51-cc62-4999-b09e-1d49072a5ade · outbound

This paper cites Convergence of score-based generative modeling for general data distributions.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Convergence of score-based generative modeling for general data distributions

Reference 35

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Observation caaea4cb-1020-4e1e-ae97-fbfd933aa62f · outbound

This paper cites Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 36

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Observation 44ceb2be-82c8-46f3-9fa0-9c14fdbb80b4 · outbound

This paper cites Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 37

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Observation 7cfc3e22-900d-4d23-b50c-dcce0a6d22f8 · outbound

This paper cites Convergence of the denoising diffusion probabilistic models for general noise schedules.arXiv preprint arXiv:2406.01320, 2024.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Convergence of the denoising diffusion probabilistic models for general noise schedules.arXiv preprint arXiv:2406.01320, 2024

Reference 38

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Observation bc08428c-8ee6-430c-915b-5758157c748d · outbound

This paper cites Improved Convergence of Score-Based Diffusion Models via Prediction-Correction.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Improved Convergence of Score-Based Diffusion Models via Prediction-Correction

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:49.208636Z

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Observation ebf9bd0c-2998-4113-a87f-715fd823e79e · outbound

This paper cites Convergence rates of variational posterior distributions.The Annals of Statistics, 48(4):2180–2207, 2020.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Convergence rates of variational posterior distributions.The Annals of Statistics, 48(4):2180–2207, 2020

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:49.289529Z

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Observation 7581a142-bbd5-4b76-bbd5-98d3a66e8345 · outbound

This paper cites Binary latent diffusion.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Binary latent diffusion

Reference 41

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Observation 8e525f70-be23-4fd8-b6bb-4e308bcaa8ab · outbound

This paper cites The rise of nonnegative matrix factorization: Algorithms and applications.Information Systems, 123:102379, 2024.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings The rise of nonnegative matrix factorization: Algorithms and applications.Information Systems, 123:102379, 2024

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:49.544493Z

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Observation f944334a-9b33-4d48-9c1c-0f8d03d06d6b · outbound

This paper cites Simplicial closure and higher-order link prediction.Proceedings of the National Academy of Sciences, 115(48):E11221–E11230, 2018.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Simplicial closure and higher-order link prediction.Proceedings of the National Academy of Sciences, 115(48):E11221–E11230, 2018

Reference 43

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Observation 1213d4d4-0c20-45aa-b377-a4f9a4f8b03f · outbound

This paper cites Structured denoising diffusion models in discrete state-spaces.Advances in neural information processing systems, 34:17981–17993, 2021.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Structured denoising diffusion models in discrete state-spaces.Advances in neural information processing systems, 34:17981–17993, 2021

Reference 44

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Observation ec0ff3de-724c-440e-939c-7ebc8032941c · outbound

This paper cites Argmax flows and multinomial diffusion: Learning categorical distributions.Advances in neural information processing systems, 34:12454–12465, 2021.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Argmax flows and multinomial diffusion: Learning categorical distributions.Advances in neural information processing systems, 34:12454–12465, 2021

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:49.909117Z

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Observation 05d80e5c-b911-4328-8f03-f3de54f1bb8f · outbound

This paper cites Algorithms for non-negative matrix factorization.Advances in neural information processing systems, 13, 2000.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Algorithms for non-negative matrix factorization.Advances in neural information processing systems, 13, 2000

Reference 46

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Observation f5bad882-5c5e-4797-bc2f-bc0cf18bb913 · outbound

This paper cites an unresolved cited work.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Unresolved cited work

Reference 47

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Observation 0ce4d956-1a9e-4359-ad45-dd0a82e401ac · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Momentum contrast for unsupervised visual representation learning

Reference 48

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Observation 1a40d135-fbcb-43ef-a1da-fe3e5962c80f · outbound

This paper cites Negative Sampling for Contrastive Representation Learning: A Review.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Negative Sampling for Contrastive Representation Learning: A Review

Reference 49

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Observation 7206c5ad-fa89-4bb9-bef7-7abae4e6048c · outbound

This paper cites aρ logb ρ −log Γ(a ρ) + (aρ −1) ψ(˜aρj )−log ˜bρj −b ρ ˜aρj ˜bρj !# ,(59) and the last prior is Eq[logp(β)] = mX j=1 KX k=1.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings aρ logb ρ −log Γ(a ρ) + (aρ −1) ψ(˜aρj )−log ˜bρj −b ρ ˜aρj ˜bρj !# ,(59) and the last prior is Eq[logp(β)] = mX j=1 KX k=1

Reference 50

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Observation 307652b5-1ac7-4421-83a1-23312dc7397c · outbound

This paper cites log ( dPRm dP bRm (Rm) )# +E PRm.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings log ( dPRm dP bRm (Rm) )# +E PRm

Reference 51

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Observation b3202a65-ae8e-4e09-a0bf-e175df2955ad · outbound

This paper cites Summing Eq.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Summing Eq

Reference 52

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Observation a9e6a980-47d6-40d2-b183-3a958224ac09 · outbound

This paper cites (131) viaP (nm) ϑ =P (nm) Φ(ϑ).

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings (131) viaP (nm) ϑ =P (nm) Φ(ϑ)

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:50.556704Z

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