Paper Citation Record · LEDGER

Graffe: Graph Representation Learning via Diffusion Probabilistic Models

As of 22 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 1 inbound Pith citation observation for arXiv:2505.04956.

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

pith.paper-citation-record.v1
2505.04956 v1

Coverage vector

measured 69 of 69 reference resolution

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T09:07:39.235976Z

Reference resolution

69 of 69 outbound references displayed

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External citation measurements

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Outbound references

Observation bb3e3f97-773a-4066-b089-ce7d264cfba3 · outbound

This paper cites Reducing the dimensionality of data with neural networks,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Reducing the dimensionality of data with neural networks,

Reference 1

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Observation 77834efe-3099-4539-826c-34a3170e5e0f · outbound

This paper cites Auto-Encoding Variational Bayes.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Auto-Encoding Variational Bayes

Reference 2

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Observation fe659483-a685-4cd4-ab02-17ccd7109120 · outbound

This paper cites Improving language understanding by generative pre- training,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Improving language understanding by generative pre- training,

Reference 3

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Observation f40ad50c-6ff0-4009-a25e-682d5e885d6b · outbound

This paper cites Generative adversarial networks,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Generative adversarial networks,

Reference 4

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This paper cites Generative pretraining from pixels,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Generative pretraining from pixels,

Reference 5

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Observation c821063a-9e9e-4ef3-9909-7e63314faaff · outbound

This paper cites Large scale adversarial representation learning,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Large scale adversarial representation learning,

Reference 6

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Observation 74e3dc5c-e862-41ea-8cc7-65a0dbc4b8c7 · outbound

This paper cites Denoising diffusion probabilistic models,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Denoising diffusion probabilistic models,

Reference 7

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Observation fee44052-47ed-43aa-b043-55e5a426f7a0 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 8

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Observation b3f456ef-0d07-4bd6-aa32-2e5187955ee9 · outbound

This paper cites A revision bloom’s taxonomy: An overview,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models A revision bloom’s taxonomy: An overview,

Reference 9

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This paper cites Image generation from scene graphs,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Image generation from scene graphs,

Reference 10

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Observation c3c1c703-e7ac-4380-bdf8-a7871cb83356 · outbound

This paper cites Infodiffusion: Representation learning using information maximizing diffusion models,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Infodiffusion: Representation learning using information maximizing diffusion models,

Reference 11

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Observation e237d075-e0e9-431b-8b31-619e1a5d996e · outbound

This paper cites Soda: Bottleneck diffusion models for representation learning,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Soda: Bottleneck diffusion models for representation learning,

Reference 12

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Observation 5e62125f-f82f-4380-bc26-0589abe300f5 · outbound

This paper cites A Survey of Graph Meets Large Language Model: Progress and Future Directions.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models A Survey of Graph Meets Large Language Model: Progress and Future Directions

Reference 13

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Observation fb991980-6c9f-40a9-b175-8b5e21a5f0b0 · outbound

This paper cites Gslb: The graph structure learning benchmark,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Gslb: The graph structure learning benchmark,

Reference 14

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Observation dd15c1e3-f15e-4981-abce-1c13299e8759 · outbound

This paper cites Denoising diffusion autoencoders are unified self-supervised learners,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Denoising diffusion autoencoders are unified self-supervised learners,

Reference 15

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Observation 123cce83-0199-4f95-8166-3bbf0feb0f2c · outbound

This paper cites Deconstructing Denoising Diffusion Models for Self-Supervised Learning.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 16

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Observation ff2da5d1-f0c2-4028-bde9-6ec4bf11fcb5 · outbound

This paper cites Directional diffusion models for graph representation learning,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Directional diffusion models for graph representation learning,

Reference 17

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Observation 059a486e-49bc-4622-a012-6de5cac1ed13 · outbound

This paper cites Diffusion-Based Representation Learning.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Diffusion-Based Representation Learning

Reference 18

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Observation e62be717-2f42-4ae6-849c-f3d8bfe5b912 · outbound

This paper cites Self-organization in a perceptual network,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Self-organization in a perceptual network,

Reference 19

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Observation edfcdab7-4501-4f2f-ba3b-44b532181378 · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Learning deep representations by mutual information estimation and maximization

Reference 20

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This paper cites Deep graph infomax.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Deep graph infomax

Reference 21

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Observation df181d8b-de57-45b7-abdd-90f84a0f42a9 · outbound

This paper cites InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Reference 22

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This paper cites Deep Graph Contrastive Representation Learning.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Deep Graph Contrastive Representation Learning

Reference 23

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Observation 1a224e62-5dd1-4b1b-bf54-2cfb380bdd9e · outbound

This paper cites Graph contrastive learning with adaptive augmentation,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Graph contrastive learning with adaptive augmentation,

Reference 24

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Observation e43bb5a0-3d79-46b1-bd2d-239b381a921f · outbound

This paper cites Graph contrastive learning with augmentations,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Graph contrastive learning with augmentations,

Reference 25

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Observation 97f19f52-3916-448a-81c6-71fc8063c87e · outbound

This paper cites Large-Scale Representation Learning on Graphs via Bootstrapping.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Large-Scale Representation Learning on Graphs via Bootstrapping

Reference 26

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Observation 1a3094e6-f22e-4ac8-9594-3a1041a121ea · outbound

This paper cites From canonical correlation analysis to self-supervised graph neural networks,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models From canonical correlation analysis to self-supervised graph neural networks,

Reference 27

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Observation 6699a1a8-eada-4f01-8fc5-fb38481c965c · outbound

This paper cites Gpt-gnn: Generative pre-training of graph neural networks,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Gpt-gnn: Generative pre-training of graph neural networks,

Reference 28

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Observation e0e27814-5b54-493c-8de8-eef46b5033dc · outbound

This paper cites Contrastive multi-view representation learning on graphs,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Contrastive multi-view representation learning on graphs,

Reference 29

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Observation 6e7a441d-1fac-4504-84a0-811bbc1dcf3f · outbound

This paper cites Gcc: Graph contrastive coding for graph neural network pre-training,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Gcc: Graph contrastive coding for graph neural network pre-training,

Reference 30

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Observation ec6c4847-3944-4f92-b3a2-e26de15bd7b7 · outbound

This paper cites Variational Graph Auto-Encoders.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Variational Graph Auto-Encoders

Reference 31

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Observation 8ce9773b-9534-47f4-92a4-415fe91b9b8f · outbound

This paper cites Graph Attention Auto-Encoders.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Graph Attention Auto-Encoders

Reference 32

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This paper cites Graphmae: Self-supervised masked graph autoencoders,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Graphmae: Self-supervised masked graph autoencoders,

Reference 33

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Observation 867b7a54-f72c-4737-9a27-4c206f4630c8 · outbound

This paper cites Uncovering neural scaling laws in molecular representation learning,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Uncovering neural scaling laws in molecular representation learning,

Reference 34

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Observation 8a1a20cc-9e89-4684-92f3-ef9178831d37 · outbound

This paper cites Beyond efficiency: Molecular data pruning for enhanced generalization,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Beyond efficiency: Molecular data pruning for enhanced generalization,

Reference 35

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Observation 49794c5e-d653-4faa-8858-d2f971009ce7 · outbound

This paper cites Gder: Safeguarding efficiency, balancing, and robustness via prototypical graph pruning,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Gder: Safeguarding efficiency, balancing, and robustness via prototypical graph pruning,

Reference 36

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Observation a1cb770c-f09b-4d94-a5f2-b6990ef69a98 · outbound

This paper cites Diffusion autoencoders: Toward a meaningful and decodable representa- tion,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Diffusion autoencoders: Toward a meaningful and decodable representa- tion,

Reference 37

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Observation 49fa50a2-a44a-4592-ba88-e678e841389c · outbound

This paper cites Unsupervised representation learning from pre-trained diffusion probabilistic models,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Unsupervised representation learning from pre-trained diffusion probabilistic models,

Reference 38

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Observation c6e5d3f6-fd36-468f-ab9f-e2483371c72e · outbound

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Diffusion models as masked autoencoders,

Reference 39

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Observation 96ef06ca-2ca3-4428-8174-692ad75e869b · outbound

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Reverse-time diffusion equation models,

Reference 40

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,

Reference 41

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models

Reference 42

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Accelerating diffusion sampling with optimized time steps,

Reference 43

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This paper cites On variational bounds of mutual information,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models On variational bounds of mutual information,

Reference 44

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Diffusion models beat gans on image synthesis,

Reference 45

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Diffusion probabilistic model made slim,

Reference 46

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Freeu: Free lunch in diffusion u-net,

Reference 47

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Diffusion is spectral autoregression,

Reference 48

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This paper cites Rethinking graph neural networks for anomaly detection,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Rethinking graph neural networks for anomaly detection,

Reference 49

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This paper cites Graphmae2: A decoding-enhanced masked self-supervised graph learner,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Graphmae2: A decoding-enhanced masked self-supervised graph learner,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:23:46.994730Z

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Observation fd52b25d-530f-4dec-a624-e1459151b353 · outbound

This paper cites Masked graph autoencoder with non-discrete bandwidths,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Masked graph autoencoder with non-discrete bandwidths,

Reference 51

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Observation 3030396c-6537-494e-8737-38dd862aeace · outbound

This paper cites Graph attention networks,.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Graph attention networks,

Reference 52

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Observation 5294b02e-b578-4554-86b9-7c02bf4bc51f · outbound

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models How Powerful are Graph Neural Networks?

Reference 53

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models What’s behind the mask: Understanding masked graph modeling for graph autoencoders,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:23:46.959665Z

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models U-net: Convolutional networks for biomedical image segmentation,

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Reference 56

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Collective classification in network data,

Reference 58

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Pitfalls of Graph Neural Network Evaluation

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Open graph benchmark: Datasets for machine learning on graphs,

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Deep graph kernels,

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Libsvm: a library for support vector,

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Adam: A Method for Stochastic Optimization

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Decoupled Weight Decay Regularization

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models SGDR: Stochastic Gradient Descent with Warm Restarts

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Rethinking graph masked autoencoders through alignment and uniformity,

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Infogcl: Information- aware graph contrastive learning,

Reference 67

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models Weisfeiler-lehman graph kernels

Reference 68

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Graffe: Graph Representation Learning via Diffusion Probabilistic Models graph2vec: Learning Distributed Representations of Graphs

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Pith citing papers

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DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation cites this paper.

DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation Graffe: Graph Representation Learning via Diffusion Probabilistic Models

Reference 4

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