Paper Citation Record · LEDGER

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?

As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2502.02488.

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

pith.paper-citation-record.v1
2502.02488 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:09:59.460695Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:49:26.565628Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact1
verified fuzzy11
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb93bbc6-bd11-4d68-b823-8efafca6c329 · outbound

This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.879963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ecd6d389-a1de-4c21-9265-1976cac231db · outbound

This paper cites S., Riley, P.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? S., Riley, P

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T12:09:59.374736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 116875ed-a614-43d2-bf35-6a452ab3a19e · outbound

This paper cites Diffusion Models for Graphs Benefit From Discrete State Spaces.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Diffusion Models for Graphs Benefit From Discrete State Spaces

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T12:09:59.379846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87c0687d-1e0f-4f77-b70d-968991d14f0f · outbound

This paper cites Graph generation with \ k 2\ -trees.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Graph generation with \ k 2\ -trees

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.755979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dba36cf3-12ee-49c6-b4c6-0d864e8e6160 · outbound

This paper cites an unresolved cited work.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-09T12:09:59.741256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 081cb439-83cc-4045-a0a3-311f243d7159 · outbound

This paper cites an unresolved cited work.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-09T12:09:59.726590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6a7d09de-6d79-4c5f-aada-b506e163e401 · outbound

This paper cites SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-09T12:09:59.521383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 586857a3-f3e3-4cee-899a-26426f28959d · outbound

This paper cites Provably powerful graph networks.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Provably powerful graph networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.710281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03ba67b2-696f-405a-9139-524f3f170f1b · outbound

This paper cites Invariant and equivariant graph networks.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Invariant and equivariant graph networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.693674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d423ac7e-f36c-4e57-af5c-e09bdd0ef8aa · outbound

This paper cites L., Lenssen, J.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? L., Lenssen, J

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.678209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 59996518-6cab-4bc3-b552-7e727009d4dc · outbound

This paper cites Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.662690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 35d8989d-f8d7-4ba0-bfd8-e9c67306668a · outbound

This paper cites Permutation invariant graph generation via score-based generative modeling.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Permutation invariant graph generation via score-based generative modeling

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.646999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3f4f03b1-e143-44d6-b7b8-d396bb886ee3 · outbound

This paper cites T., Maron, H., and Lipman, Y.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? T., Maron, H., and Lipman, Y

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.631392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c22c8a3d-2576-453a-b314-88cb9ac72820 · outbound

This paper cites DiGress: Discrete Denoising diffusion for graph generation.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? DiGress: Discrete Denoising diffusion for graph generation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T12:09:59.433936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242660b4-f33b-4a3c-b463-1b4aa4892b15 · outbound

This paper cites How powerful are graph neural networks? In ICLR, 2019.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? How powerful are graph neural networks? In ICLR, 2019

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T12:09:59.438896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fded0986-897f-4d18-9b39-aee602f214ce · outbound

This paper cites A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T12:09:59.443290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fbbd0ab-79be-4878-a3ef-781f4b8e49f9 · outbound

This paper cites and Li, P.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? and Li, P

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.597039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14fa961a-dd97-4bc6-8703-44aaedca87cd · outbound

This paper cites From relational pooling to subgraph GNN s: A universal framework for more expressive graph neural networks.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? From relational pooling to subgraph GNN s: A universal framework for more expressive graph neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.581580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0e52d745-9559-478c-b787-425513a14caf · outbound

This paper cites Latent graph diffusion: A unified framework for generation and prediction on graphs.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Latent graph diffusion: A unified framework for generation and prediction on graphs

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:09:59.564285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f5d1719e-1101-419f-8c68-09820c5ca0df · outbound

This paper cites write newline.

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? write newline

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T12:09:59.460695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c0f90b13-d5ef-4375-854a-335fd23a8b6b · inbound

Permutation-Invariant Spectral Learning via Dyson Diffusion cites this paper.

Permutation-Invariant Spectral Learning via Dyson Diffusion Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T10:49:26.565628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.