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Paper Citation Record · LEDGER

Stability of Flow Models for Graph Signals

As of 21 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2607.07510.

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

pith.paper-citation-record.v1
2607.07510 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-09T08:52:43.105057Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

53 of 53 outbound references displayed

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  • verified fuzzy48
  • unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e6b11651-bf3b-4946-b1af-7e13e3465176 · outbound

This paper cites Flow matching for generative modeling,.

Stability of Flow Models for Graph Signals Flow matching for generative modeling,

Reference 1

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Observation 4dc28873-6bf4-457e-b28d-7a9b36997d0e · outbound

This paper cites Denoising diffusion probabilistic models,.

Stability of Flow Models for Graph Signals Denoising diffusion probabilistic models,

Reference 2

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Observation 919881b7-1c04-40d8-b14e-30d8952db382 · outbound

This paper cites Neural ordinary differential equations,.

Stability of Flow Models for Graph Signals Neural ordinary differential equations,

Reference 3

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Observation aff71566-db53-41bf-b78c-79771c89ee21 · outbound

This paper cites Graph normalizing flows,.

Stability of Flow Models for Graph Signals Graph normalizing flows,

Reference 4

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Observation 0244a2ae-d46b-4821-8ac1-c2ef3f4ba537 · outbound

This paper cites Permu- tation invariant graph generation via score-based generative modeling,.

Stability of Flow Models for Graph Signals Permu- tation invariant graph generation via score-based generative modeling,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 03634508-a55e-4bfd-8b45-66a3787873bb · outbound

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

Stability of Flow Models for Graph Signals Score-based generative modeling of graphs via the system of stochastic differential equations,

Reference 6

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Source-reported events for the cited work

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Observation 0b9082e2-6890-464d-9f0c-325f411f6a54 · outbound

This paper cites Generative diffusion models on graphs: Methods and applications,.

Stability of Flow Models for Graph Signals Generative diffusion models on graphs: Methods and applications,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation ad5018a1-2c8d-43f1-9cc6-66fb71426408 · outbound

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

Stability of Flow Models for Graph Signals Digress: Discrete denoising diffusion for graph generation,

Reference 8

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 5d238831-2451-4c84-9ca0-1b63a2f858d3 · outbound

This paper cites Prior-informed flow matching for graph reconstruction,.

Stability of Flow Models for Graph Signals Prior-informed flow matching for graph reconstruction,

Reference 9

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d84d195e-1658-449d-bedf-4de6c0cddf27 · outbound

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

Stability of Flow Models for Graph Signals Graph-aware diffusion for signal generation,

Reference 10

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Observation aa3ea01e-d888-4c2d-bae4-95b93a3d640a · outbound

This paper cites Graph signal generative diffusion models,.

Stability of Flow Models for Graph Signals Graph signal generative diffusion models,

Reference 11

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Observation f7017ed7-f338-4620-bfae-90d729db91c1 · outbound

This paper cites Graph signal diffusion model for collaborative filtering,.

Stability of Flow Models for Graph Signals Graph signal diffusion model for collaborative filtering,

Reference 12

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Observation a2c3ac4e-1b0d-4273-9790-03d7d7d8d5a9 · outbound

This paper cites Topological schr ¨odinger bridge matching,.

Stability of Flow Models for Graph Signals Topological schr ¨odinger bridge matching,

Reference 13

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation eefbca80-4252-40eb-a037-521a5d7793be · outbound

This paper cites Graph signal processing: Overview, challenges, and applications,.

Stability of Flow Models for Graph Signals Graph signal processing: Overview, challenges, and applications,

Reference 14

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Observation d5bb331a-3371-4e83-9177-12b714396df8 · outbound

This paper cites A graph signal processing perspective on functional brain imaging,.

Stability of Flow Models for Graph Signals A graph signal processing perspective on functional brain imaging,

Reference 15

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 62191712-29d1-4a4a-8bc0-e0f787ab55ec · outbound

This paper cites Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,.

Stability of Flow Models for Graph Signals Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,

Reference 16

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Observation 54a61c8c-7a46-4777-948e-71e9b60782de · outbound

This paper cites Graph signal processing in applications to sensor networks, smart grids, and smart cities,.

Stability of Flow Models for Graph Signals Graph signal processing in applications to sensor networks, smart grids, and smart cities,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 09dfc9dc-afcd-43d5-9830-cb144e3a2455 · outbound

This paper cites Grid-graph signal processing (Grid- GSP): A graph signal processing framework for the power grid,.

Stability of Flow Models for Graph Signals Grid-graph signal processing (Grid- GSP): A graph signal processing framework for the power grid,

Reference 18

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation fc0b109b-460f-42c2-ba54-bf49343d6b76 · outbound

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

Stability of Flow Models for Graph Signals Graph Signal Diffusion Models for Wireless Resource Allocation

Reference 19

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Observation 5f46f40e-0966-4e39-a2f8-9c433ba0c0d4 · outbound

This paper cites Data augmentation in classification and segmentation: A survey and new strategies,.

Stability of Flow Models for Graph Signals Data augmentation in classification and segmentation: A survey and new strategies,

Reference 20

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 4a823a87-10fc-4953-8c8c-1420317e70a5 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Stability of Flow Models for Graph Signals Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 21

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 46f25580-dff4-4685-86be-3cd797ff6f94 · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Stability of Flow Models for Graph Signals Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 22

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Observation 398c2962-a756-4690-ad61-6e31cfc66871 · outbound

This paper cites Intriguing properties of neural networks,.

Stability of Flow Models for Graph Signals Intriguing properties of neural networks,

Reference 23

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 0cb5fa01-2312-4406-af68-ef37eba91cba · outbound

This paper cites Stability and generalization.

Stability of Flow Models for Graph Signals Stability and generalization

Reference 24

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d03f158a-1aae-485b-a3c8-67b660c55bfd · outbound

This paper cites Adversarial attacks on neural networks for graph data,.

Stability of Flow Models for Graph Signals Adversarial attacks on neural networks for graph data,

Reference 25

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 66c1faf1-b372-47c3-ae34-487db851dc84 · outbound

This paper cites an unresolved cited work.

Stability of Flow Models for Graph Signals Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 47b35b6b-6675-4a09-9157-b250fc373e94 · outbound

This paper cites On the use of correlation as a measure of network connectivity,.

Stability of Flow Models for Graph Signals On the use of correlation as a measure of network connectivity,

Reference 27

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9d03f4a3-4ac0-44bb-bfe9-02ba35d2319a · outbound

This paper cites Connecting the dots: Identifying network structure via graph signal processing,.

Stability of Flow Models for Graph Signals Connecting the dots: Identifying network structure via graph signal processing,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 62fe9c71-52f6-4e1c-8d1f-79eaf8b71493 · outbound

This paper cites Learning graphs from data: A signal representation perspective.

Stability of Flow Models for Graph Signals Learning graphs from data: A signal representation perspective

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation cc5390f5-d5bf-4552-8b61-f7bbd4231a6f · outbound

This paper cites Stability properties of graph neural networks,.

Stability of Flow Models for Graph Signals Stability properties of graph neural networks,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 2bf99ede-c9a4-4ff1-8c33-c75d29cd3dbb · outbound

This paper cites Transferability properties of graph neural networks,.

Stability of Flow Models for Graph Signals Transferability properties of graph neural networks,

Reference 31

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation a6b1097f-c86a-4650-a168-edd4d84836e4 · outbound

This paper cites Interpretable stability bounds for spectral graph filters,.

Stability of Flow Models for Graph Signals Interpretable stability bounds for spectral graph filters,

Reference 32

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 2be2100d-a794-45a3-b699-76a4bfb25a34 · outbound

This paper cites Trans- ferability of spectral graph convolutional neural networks,.

Stability of Flow Models for Graph Signals Trans- ferability of spectral graph convolutional neural networks,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 1ba2b856-0b8c-455a-9df3-a7222b15c976 · outbound

This paper cites Equivariant flows: Exact likelihood generative learning for symmetric densities,.

Stability of Flow Models for Graph Signals Equivariant flows: Exact likelihood generative learning for symmetric densities,

Reference 34

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raw_fallback, observed 2026-07-09T08:56:06.618411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation e1ed388f-f4e4-4856-8b35-e9588abdbaf3 · outbound

This paper cites Graph filters for signal processing and machine learning on graphs,.

Stability of Flow Models for Graph Signals Graph filters for signal processing and machine learning on graphs,

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9c4e7071-0790-4fee-9bbc-f72d35b17e33 · outbound

This paper cites Convolutional neural network architectures for signals supported on graphs,.

Stability of Flow Models for Graph Signals Convolutional neural network architectures for signals supported on graphs,

Reference 36

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raw_fallback, observed 2026-07-09T08:56:06.660923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:8be2b54883fcde9fe240cf020b12f19b12054f99559f4cda4d6862249e2804a9

Observation 028ae26f-21ad-4cce-aa67-9c8624d1a25c · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Stability of Flow Models for Graph Signals Semi-supervised classification with graph convolutional networks,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.658953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:3fc8ff7e22365fe223f3d42438bbb40598cfa7cf9b05b9517fd899706329bbb1

Observation fb56dd24-d73f-4a28-a3f2-2391db2e4173 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering,.

Stability of Flow Models for Graph Signals Convolutional neural networks on graphs with fast localized spectral filtering,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.672659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:68fa567b8b8d210101e3de067ce064620882aba0e302bb18a4bd30adf6a40535

Observation 4a1467e7-2005-4a0b-84d7-cf29b895993e · outbound

This paper cites Graph neural networks: Architec- tures, stability and transferability,.

Stability of Flow Models for Graph Signals Graph neural networks: Architec- tures, stability and transferability,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.670639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:b3662750bd4493e27deaab94c3ef174738528d3ee64417be2610ecf4831aced5

Observation 177906cd-de83-4ec6-b71b-a317b8d6dca8 · outbound

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

Stability of Flow Models for Graph Signals Score-based generative modeling through stochastic differ- ential equations,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.616624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:2c92b7bea580c3ea03664575cb3b5742328f43dce6e1d49fe0a2979ea12339f7

Observation 0ef97fc9-7135-4189-afc9-74bb2e05be24 · outbound

This paper cites Improved denoising diffusion probabilis- tic models,.

Stability of Flow Models for Graph Signals Improved denoising diffusion probabilis- tic models,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.613368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:ecffdf5dafbd75d64dcc7696eedc45d29caee50f4bc57aac6634bc31f39f6aef

Observation 6689c14d-840e-4d11-a464-c97102bc9d7c · outbound

This paper cites Villaniet al.,Optimal transport: old and new.

Stability of Flow Models for Graph Signals Villaniet al.,Optimal transport: old and new

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:a5a3e3e3197373df59feaa16d16481c0256ae4a960b046438b5d43c305889258

Observation b6ea9668-43b0-4975-8328-d48c7ac69965 · outbound

This paper cites an unresolved cited work.

Stability of Flow Models for Graph Signals Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-07-09T08:56:06.664510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:182f7342ba17d386c81d283931fd92dfeecf7cdc22c2b1e906a0005ebb1e2e04

Observation a1410777-acd9-48a2-baa8-172bcfe4ed1a · outbound

This paper cites Robust graph neural networks via probabilistic lipschitz constraints,.

Stability of Flow Models for Graph Signals Robust graph neural networks via probabilistic lipschitz constraints,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.620065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:7d4d8c80841b8b5faf4dac7e0c9dc34cd9b89ca625567c3ebfe877ae4e9e4a72

Observation 03d54a4b-af3f-457b-aa43-e00f4385087a · outbound

This paper cites Training stable graph neural networks through constrained learning,.

Stability of Flow Models for Graph Signals Training stable graph neural networks through constrained learning,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.668510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:1b16d050d17dafdd25445ab848e9aca91cd238cf3fb3464662942caf2f68ac78

Observation 888202af-1f4a-46ce-bbf0-a7e36d5e242f · outbound

This paper cites an unresolved cited work.

Stability of Flow Models for Graph Signals Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-07-09T08:56:06.625125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:5eddd6009601f93cf78483b2f6f6e2ddf72dd1d46105dd700c12682554517426

Observation 21557102-6c62-4a5c-9520-7e4fceb82b9c · outbound

This paper cites Stochastic blockmodels: First steps.

Stability of Flow Models for Graph Signals Stochastic blockmodels: First steps

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.599180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:b760c28ff401efeaa4778589a6acb1158c499a36c7bd22e12c7040cea49e3300

Observation 46f9197f-b8a1-4b35-aa2f-e035db58d78d · outbound

This paper cites Graph frequency analysis of brain signals,.

Stability of Flow Models for Graph Signals Graph frequency analysis of brain signals,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.676462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:f785008fb51a998c223d6a95b755a9b2a81642ac3cb6c6a79063b02a4e0f6f03

Observation b915525d-2bf3-4622-815f-68bdc71dcf5d · outbound

This paper cites Decoupling of brain function from structure reveals regional behavioral specialization in humans,.

Stability of Flow Models for Graph Signals Decoupling of brain function from structure reveals regional behavioral specialization in humans,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.666507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:7947dae2f0ffb01ad88c5145582f5b52aabd8bbf2323d4c69abf77468c061c2e

Observation 1818fe5a-66ee-46e5-b821-fdaeb46ad8f5 · outbound

This paper cites Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,.

Stability of Flow Models for Graph Signals Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.614996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:8221dce6a5eca9aab6437a9065d4fc7cd7c492f49d7e96ad4ef481daaba11e24

Observation 76274b02-b98e-40a3-a2e9-4f1d61cbf06e · outbound

This paper cites A kernel two-sample test.

Stability of Flow Models for Graph Signals A kernel two-sample test

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.623380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:b84dd9179ed4235256e335052743ab48c1e5e51b96d48fc62f5e0df544f89623

Observation dec15c13-a4b5-4e4b-8240-1678b11a4eaa · outbound

This paper cites Lipschitz regularity of deep neural networks: Analysis and efficient estimation,.

Stability of Flow Models for Graph Signals Lipschitz regularity of deep neural networks: Analysis and efficient estimation,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.608388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:d6f64b51410518f54f7ad6bc36e884e10ebbb650d897f4ccefaf3ff13145fcb4

Observation 502af415-ea88-4d05-a665-00af205671ec · outbound

This paper cites Learning by transference: Training graph neural networks on growing graphs,.

Stability of Flow Models for Graph Signals Learning by transference: Training graph neural networks on growing graphs,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T08:56:06.600729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-09T08:52:43.105057Z digest=sha256:a9661329c3aa3c96560a6218ff676a9f64f1febc0c63195c2388d35daae9d342

Pith citing papers

No inbound Pith citation observations are available.