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Paper Citation Record · LEDGER

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.16365.

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

pith.paper-citation-record.v1
2505.16365 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:08:18.784987Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

37 of 37 outbound references displayed

  • verified exact7
  • verified fuzzy2
  • unresolved27
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a554b9f0-92e9-4d32-89e2-955d7eec85e6 · outbound

This paper cites & Ranganathan, R.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules & Ranganathan, R

Reference 2

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Observation 92f342e5-b9ba-4965-8b35-3b34ad724b9d · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Unresolved cited work

Reference 3

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Observation 45263328-f453-479f-a32d-35784908cb5d · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules & Wang, B

Reference 4

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Observation ab873fcb-b404-4e2e-9664-754514929786 · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Unresolved cited work

Reference 5

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Source-reported events for the cited work

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Observation cdfd5a5a-6553-48b4-8832-6d7228c3d96f · outbound

This paper cites G., Madzhidov, T.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules G., Madzhidov, T

Reference 6

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Source-reported events for the cited work

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Observation 2568dccd-1494-4b68-8d01-eaae7cd38e77 · outbound

This paper cites an unresolved cited work.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation 2b6bf1eb-f97f-407a-a06d-93ca2a085b59 · outbound

This paper cites T ., Davies, D.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules T ., Davies, D

Reference 8

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Source-reported events for the cited work

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Observation 3ca4efbf-1d75-4d1f-be2d-ff2e633e4859 · outbound

This paper cites Reinvent 2.0: An ai tool for de novo drug design.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Reinvent 2.0: An ai tool for de novo drug design

Reference 9

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Source-reported events for the cited work

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Observation c4a74f93-c616-48d5-aea8-d88d8afd83de · outbound

This paper cites Junction Tree Variational Autoencoder for Molecular Graph Generation.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Junction Tree Variational Autoencoder for Molecular Graph Generation

Reference 11

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Source-reported events for the cited work

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Observation 94dab1a8-3e05-4516-acf6-f6050085e6f1 · outbound

This paper cites MolGAN: An implicit generative model for small molecular graphs.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules MolGAN: An implicit generative model for small molecular graphs

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3124660e-e8ca-4094-9497-7b810df906ea · outbound

This paper cites Scalable Deep Generative Modeling for Sparse Graphs.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Scalable Deep Generative Modeling for Sparse Graphs

Reference 14

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verified exact
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Source-reported events for the cited work

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Observation 68573b5a-659f-4d21-b045-c7601a48f7b7 · outbound

This paper cites GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 25c49867-3233-4757-bdcb-1c9f5c74c6f6 · outbound

This paper cites Exploring Chemical Space with Score-based Out-of-distribution Generation.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Exploring Chemical Space with Score-based Out-of-distribution Generation

Reference 17

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Observation 77a4ad63-4be6-47bf-9bf0-b5467240579c · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 02b0c667-12f7-49d4-9d25-2725256727d5 · outbound

This paper cites an unresolved cited work.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 9e5ad02f-13c7-4bc8-a277-3c4eb8f58e2a · outbound

This paper cites Zero-Shot Text-to-Image Generation.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Zero-Shot Text-to-Image Generation

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5d799b43-aaad-4452-9ac8-ba00a364186f · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9fb602e6-ef84-478c-9c84-7f43f6f168ef · outbound

This paper cites Structured Denoising Diffusion Models in Discrete State-Spaces.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Structured Denoising Diffusion Models in Discrete State-Spaces

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 18bfeae0-52f1-48ab-8eed-6efa7fa48e7c · outbound

This paper cites Equivariant Diffusion for Molecule Generation in 3D.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Equivariant Diffusion for Molecule Generation in 3D

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 71190212-aa2e-4cb1-af77-559c0d05bec9 · outbound

This paper cites Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Reference 26

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Source-reported events for the cited work

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Observation 348daff6-b253-433c-a4aa-b4aa4ee784b1 · outbound

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

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Diffusion Models for Graphs Benefit From Discrete State Spaces

Reference 27

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Source-reported events for the cited work

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Observation 182bfb17-8aa2-445e-8191-a5ba66c22823 · outbound

This paper cites Sparse Training of Discrete Diffusion Models for Graph Generation.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Sparse Training of Discrete Diffusion Models for Graph Generation

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0385413a-25ad-4735-980f-6123407522d2 · outbound

This paper cites Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

Reference 30

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verified exact
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Source-reported events for the cited work

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Observation 6301be82-f87e-409f-b983-2d64da8bb474 · outbound

This paper cites an unresolved cited work.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Unresolved cited work

Reference 31

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Observation 80c86d34-9321-4eeb-b296-565f83b75971 · outbound

This paper cites MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Reference 32

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Observation 3be91e9c-ee05-4910-b05d-3a092de73d5c · outbound

This paper cites & Sneppen, K.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules & Sneppen, K

Reference 33

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Observation ba526c4b-5658-4821-9361-2a5722dd22d9 · outbound

This paper cites On the uniform generation of random graphs with prescribed degree sequences.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules On the uniform generation of random graphs with prescribed degree sequences

Reference 34

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Source-reported events for the cited work

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Observation ee763318-eed8-4e68-8028-2d00aaaca251 · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules R., Mezei, T

Reference 35

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Source-reported events for the cited work

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Observation 8309a297-f087-44ff-abb4-e7ef361d475a · outbound

This paper cites Generative Modelling of Structurally Constrained Graphs.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Generative Modelling of Structurally Constrained Graphs

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 05bbfc49-775a-4597-8b9c-2dbcda73d455 · outbound

This paper cites Diffusion Models for Constrained Domains.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Diffusion Models for Constrained Domains

Reference 37

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Observation 13abb1c5-0654-4a88-af11-0089dc7289d0 · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Metropolis Sampling for Constrained Diffusion Models

Reference 38

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verified exact
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Source-reported events for the cited work

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Observation 759fd7a7-4a2f-446f-9fb8-b77c4aab8a54 · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules & Reed, B

Reference 39

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Observation b486b0b5-6b0e-4a57-b56b-3914b28b51c5 · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Unresolved cited work

Reference 40

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ee088fca-4351-4caf-9fb9-8a37d3a29226 · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules A Survey of Deep Active Learning

Reference 42

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Observation 24c85b82-7ccb-45f8-9509-36023908dace · outbound

This paper cites Proximal Policy Optimization Algorithms.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Proximal Policy Optimization Algorithms

Reference 44

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Unavailable: canonical work link unavailable.

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Observation 6c83234d-f092-440b-a791-2b30fd022444 · outbound

This paper cites DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 696e017e-6f99-4eb6-96da-e3e8ab22d0fe · outbound

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A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Unresolved cited work

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.