Paper Citation Record · LEDGER

Generative Diffusion Models of Stochastic Graph Signals

As of 3 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2607.06833.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.06833 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-10T20:22:48.051821Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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verified fuzzy44
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3157d157-bfbd-492b-9967-f9711d2895f5 · outbound

This paper cites Graph signal generative diffusion 12 models,.

Generative Diffusion Models of Stochastic Graph Signals Graph signal generative diffusion 12 models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.980960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1e3f1c7f-3a31-4e73-a6ab-ddc27c631224 · outbound

This paper cites Graph Signal Diffusion Models for Wireless Resource Allocation.

Generative Diffusion Models of Stochastic Graph Signals Graph Signal Diffusion Models for Wireless Resource Allocation

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:27:36.625083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 062541bf-b7a9-4016-aaa0-244998cb1085 · outbound

This paper cites Neural graph collaborative filtering,.

Generative Diffusion Models of Stochastic Graph Signals Neural graph collaborative filtering,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.958831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 677c0395-cc1b-44c2-a692-5ad72c73a526 · outbound

This paper cites Ultragcn: Ultra simplification of graph convolutional networks for recommendation,.

Generative Diffusion Models of Stochastic Graph Signals Ultragcn: Ultra simplification of graph convolutional networks for recommendation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.970208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a3c1b172-43e0-4310-b9ea-2fddaf6b7d6b · outbound

This paper cites Personalized graph signal processing for collaborative filtering,.

Generative Diffusion Models of Stochastic Graph Signals Personalized graph signal processing for collaborative filtering,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.975640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a1c1c203-538f-4454-9f64-808af472c44a · outbound

This paper cites Optimal wireless resource allo- cation with random edge graph neural networks,.

Generative Diffusion Models of Stochastic Graph Signals Optimal wireless resource allo- cation with random edge graph neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.936342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 56ba35c2-8825-4806-b324-8f1122afb1b3 · outbound

This paper cites Graph neural networks for wireless communications: From theory to practice,.

Generative Diffusion Models of Stochastic Graph Signals Graph neural networks for wireless communications: From theory to practice,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.938177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 18d06fd8-1b10-416c-95b3-df021c378992 · outbound

This paper cites Link scheduling using graph neural networks,.

Generative Diffusion Models of Stochastic Graph Signals Link scheduling using graph neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.973720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8b24fe3f-2c78-4eee-a4a3-0b9d624d618f · outbound

This paper cites ENGNN: A general edge-update empowered GNN architecture for radio resource management in wireless networks,.

Generative Diffusion Models of Stochastic Graph Signals ENGNN: A general edge-update empowered GNN architecture for radio resource management in wireless networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.971990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4372877a-b18f-49b6-80c2-b78050b235d0 · outbound

This paper cites Deep graph unfolding for beamforming in mu-mimo inter- ference networks,.

Generative Diffusion Models of Stochastic Graph Signals Deep graph unfolding for beamforming in mu-mimo inter- ference networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.960945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a099f4f2-9540-4c61-b664-0a8b33261636 · outbound

This paper cites Fast state-augmented learning for wireless resource allocation with dual variable regression,.

Generative Diffusion Models of Stochastic Graph Signals Fast state-augmented learning for wireless resource allocation with dual variable regression,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.977324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f5411815-67b7-4711-b363-bd014ddb275b · outbound

This paper cites Incorporating corporation relationship via graph convolutional neural networks for stock price prediction,.

Generative Diffusion Models of Stochastic Graph Signals Incorporating corporation relationship via graph convolutional neural networks for stock price prediction,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.934601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 043339f8-2830-4b6b-858e-ee5272d21973 · outbound

This paper cites Spatiotemporal hypergraph convolution network for stock movement forecasting,.

Generative Diffusion Models of Stochastic Graph Signals Spatiotemporal hypergraph convolution network for stock movement forecasting,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.979032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ee63f6b1-ca5d-459a-9d1d-b975c61f2c43 · outbound

This paper cites Attention based dynamic graph neural network for asset pricing,.

Generative Diffusion Models of Stochastic Graph Signals Attention based dynamic graph neural network for asset pricing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.931162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2c9c937e-5867-4f10-833c-f6e1aca0f6aa · outbound

This paper cites Stationary signal processing on graphs,.

Generative Diffusion Models of Stochastic Graph Signals Stationary signal processing on graphs,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.946428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1d817e66-c5d1-46c1-a6b3-92447fe79254 · outbound

This paper cites Score-based generative modeling of graphs via the system of stochastic differential equations,.

Generative Diffusion Models of Stochastic Graph Signals Score-based generative modeling of graphs via the system of stochastic differential equations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.922649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c7cf5ddc-f01c-4f9c-aac7-8f5a8a1878c4 · outbound

This paper cites DiGress: Discrete denoising diffusion for graph generation,.

Generative Diffusion Models of Stochastic Graph Signals DiGress: Discrete denoising diffusion for graph generation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.925969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2ff0987c-c587-4975-95d8-1e0f3762f85f · outbound

This paper cites Equivariant diffusion for molecule generation in 3d,.

Generative Diffusion Models of Stochastic Graph Signals Equivariant diffusion for molecule generation in 3d,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.949756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9dc43e9f-da0c-4d23-85fb-34755148603f · outbound

This paper cites Auto-encoding variational bayes,.

Generative Diffusion Models of Stochastic Graph Signals Auto-encoding variational bayes,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.919281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 290ca8f2-ae01-4f74-adc9-e368b73b6fbc · outbound

This paper cites Generative adversarial nets,.

Generative Diffusion Models of Stochastic Graph Signals Generative adversarial nets,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.920876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5486ef4c-ef7e-4661-99e5-223653038f0b · outbound

This paper cites Variational inference with normalizing flows,.

Generative Diffusion Models of Stochastic Graph Signals Variational inference with normalizing flows,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.943092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b4fba616-1442-4959-80a5-7e24118b253b · outbound

This paper cites Denoising diffusion probabilistic models,.

Generative Diffusion Models of Stochastic Graph Signals Denoising diffusion probabilistic models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.944746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a4f3a390-e642-40d2-b3f9-5fd27a38cf0b · outbound

This paper cites Denoising diffusion implicit models,.

Generative Diffusion Models of Stochastic Graph Signals Denoising diffusion implicit models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.985558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 17668259-3549-4742-acff-75d517fb4da1 · outbound

This paper cites Score-based generative modeling through stochastic differential equations,.

Generative Diffusion Models of Stochastic Graph Signals Score-based generative modeling through stochastic differential equations,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.917461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation aa1eb3a8-63af-4c21-8bf7-0ef16f72d51f · outbound

This paper cites Flow matching for generative modeling,.

Generative Diffusion Models of Stochastic Graph Signals Flow matching for generative modeling,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.939778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 717171ce-d6d5-4954-97f1-0a9ea463ff6e · outbound

This paper cites DiffSTG: Probabilistic spatio- temporal graph forecasting with denoising diffusion models,.

Generative Diffusion Models of Stochastic Graph Signals DiffSTG: Probabilistic spatio- temporal graph forecasting with denoising diffusion models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.913965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2cad116d-994b-4ded-afb8-c6c7a8d56fd3 · outbound

This paper cites DiffSTOCK: Probabilistic relational stock market predictions using diffusion models,.

Generative Diffusion Models of Stochastic Graph Signals DiffSTOCK: Probabilistic relational stock market predictions using diffusion models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.915694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2601fc95-3d87-48a7-85d7-3ece21938c2e · outbound

This paper cites DHMoE: Diffusion generated hierar- chical multi-granular expertise for stock prediction,.

Generative Diffusion Models of Stochastic Graph Signals DHMoE: Diffusion generated hierar- chical multi-granular expertise for stock prediction,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.987254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 01a0cc31-8b7c-4312-a67c-600a06130a8c · outbound

This paper cites Graph-aware diffusion for signal generation,.

Generative Diffusion Models of Stochastic Graph Signals Graph-aware diffusion for signal generation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.941427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4c04f42f-29ff-415c-8538-d4b3f496c910 · outbound

This paper cites Diffusion model based resource allocation strategy in ultra-reliable wireless networked control systems,.

Generative Diffusion Models of Stochastic Graph Signals Diffusion model based resource allocation strategy in ultra-reliable wireless networked control systems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.955366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c9a014de-1989-4444-b52c-d41b9da351ce · outbound

This paper cites Diffsg: A generative solver for network optimization with diffusion model,.

Generative Diffusion Models of Stochastic Graph Signals Diffsg: A generative solver for network optimization with diffusion model,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.951390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation cc6b78d6-de6e-4b9d-bfd8-01cbc7674731 · outbound

This paper cites Generative diffusion models for resource allocation in wireless networks,.

Generative Diffusion Models of Stochastic Graph Signals Generative diffusion models for resource allocation in wireless networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.908698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8bc093ae-d5ca-4d6c-a9c6-74b1061e6439 · outbound

This paper cites Diffu- sion model for multiple antenna communication,.

Generative Diffusion Models of Stochastic Graph Signals Diffu- sion model for multiple antenna communication,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.910403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a4e86f09-a4ad-47fd-99cf-e198c61b48cc · outbound

This paper cites Deterministic score-based diffusion model for channel estimation in ris-assisted mimo systems,.

Generative Diffusion Models of Stochastic Graph Signals Deterministic score-based diffusion model for channel estimation in ris-assisted mimo systems,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.912109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 818aabfc-e90a-4519-adfa-a84827ed0c93 · outbound

This paper cites Gen- erating high dimensional user-specific wireless channels using diffusion models,.

Generative Diffusion Models of Stochastic Graph Signals Gen- erating high dimensional user-specific wireless channels using diffusion models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.948163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1a262ab7-89d7-4069-832b-5ceee7acc009 · outbound

This paper cites U-net: Convolu- tional networks for biomedical image segmentation,.

Generative Diffusion Models of Stochastic Graph Signals U-net: Convolu- tional networks for biomedical image segmentation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.924300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6703c5b1-4d2e-4bbf-80d2-4b00b02db856 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.

Generative Diffusion Models of Stochastic Graph Signals nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.964678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 822e9ac1-ec52-42ed-80ae-718bcaa1a29d · outbound

This paper cites Convolutional neural network architectures for signals sup- ported on graphs,.

Generative Diffusion Models of Stochastic Graph Signals Convolutional neural network architectures for signals sup- ported on graphs,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.982777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b3f44bc6-b421-4493-8432-ab5183a76906 · outbound

This paper cites Hierarchical graph representation learn- ing with differentiable pooling,.

Generative Diffusion Models of Stochastic Graph Signals Hierarchical graph representation learn- ing with differentiable pooling,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.966682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation db9673e0-f690-4924-a5d0-02f1de944128 · outbound

This paper cites Graph u-nets,.

Generative Diffusion Models of Stochastic Graph Signals Graph u-nets,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.957145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 037a8998-49a6-4f61-9493-9527038d8365 · outbound

This paper cites Striving for simplicity: The all convolutional net,.

Generative Diffusion Models of Stochastic Graph Signals Striving for simplicity: The all convolutional net,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.962926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 08d9e394-090b-458a-aaa2-85717907d416 · outbound

This paper cites Multi-scale context aggregation by dilated convolutions,.

Generative Diffusion Models of Stochastic Graph Signals Multi-scale context aggregation by dilated convolutions,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.968469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8bab9202-bf09-445d-95db-1a9b3cc1f44f · outbound

This paper cites Graph neural networks: Architectures, stability, and transferability,.

Generative Diffusion Models of Stochastic Graph Signals Graph neural networks: Architectures, stability, and transferability,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.932883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 794ed825-86b2-420e-ac23-b9c751bcfe8e · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Generative Diffusion Models of Stochastic Graph Signals Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:27:36.621059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4a81e317-bd77-45bf-8ea3-745d616a2671 · outbound

This paper cites Reversible instance normalization for accurate time- series forecasting against distribution shift,.

Generative Diffusion Models of Stochastic Graph Signals Reversible instance normalization for accurate time- series forecasting against distribution shift,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.929492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 212a7ca9-17a4-4fe7-af3c-9c0412378ccd · outbound

This paper cites Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement,.

Generative Diffusion Models of Stochastic Graph Signals Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:27:36.927748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

No inbound Pith citation observations are available.