SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion | Paper Citation Record

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This paper cites Community detection and stochastic block models: recent developments.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Community detection and stochastic block models: recent developments

Reference 1

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This paper cites and Mostajabdaveh, M.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Mostajabdaveh, M

Reference 2

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This paper cites D., Ho, J., Tarlow, D., and Van Den Berg, R.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion D., Ho, J., Tarlow, D., and Van Den Berg, R

Reference 3

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This paper cites and Albert, R.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Albert, R

Reference 4

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This paper cites Generative Code Modeling with Graphs.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Generative Code Modeling with Graphs

Reference 5

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This paper cites Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 6

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This paper cites Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling

Reference 7

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This paper cites K., and Lu, X.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion K., and Lu, X

Reference 8

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This paper cites Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks

Reference 9

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Unresolved cited work

Reference 10

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This paper cites Contextual stochastic block models.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Contextual stochastic block models

Reference 11

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This paper cites and Nichol, A.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Nichol, A

Reference 12

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Unresolved cited work

Reference 13

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This paper cites On the evolution of random graphs.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion On the evolution of random graphs

Reference 14

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This paper cites Algebraic connectivity of graphs.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Algebraic connectivity of graphs

Reference 15

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This paper cites M., Rasch, M.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion M., Rasch, M

Reference 16

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This paper cites Graphite: Iterative generative modeling of graphs.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Graphite: Iterative generative modeling of graphs

Reference 17

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This paper cites L., Ying, R., and Leskovec, J.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion L., Ying, R., and Leskovec, J

Reference 18

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 19

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This paper cites Denoising diffusion probabilistic models.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Denoising diffusion probabilistic models

Reference 20

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This paper cites W., Laskey, K.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion W., Laskey, K

Reference 21

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This paper cites Argmax flows and multinomial diffusion: Learning categorical distributions.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Argmax flows and multinomial diffusion: Learning categorical distributions

Reference 22

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This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 23

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Reference 24

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This paper cites Graph Generation with Diffusion Mixture.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Graph Generation with Diffusion Mixture

Reference 25

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This paper cites An algorithm for drawing general undirected graphs.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion An algorithm for drawing general undirected graphs

Reference 26

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Newman, M

Reference 27

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Kumar, V

Reference 28

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion H., Vygen, J., Korte, B., and Vygen, J

Reference 29

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This paper cites Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets

Reference 30

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This paper cites Multi-objective de novo drug design with conditional graph generative model.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Multi-objective de novo drug design with conditional graph generative model

Reference 31

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators

Reference 32

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Reference 33

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Reference 34

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion E., Watts, D

Reference 35

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 36

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Permutation invariant graph generation via score-based generative modeling

Reference 37

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Deepwalk: Online learning of social representations

Reference 38

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This paper cites Fr \'e chet chemnet distance: a metric for generative models for molecules in drug discovery.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Fr \'e chet chemnet distance: a metric for generative models for molecules in drug discovery

Reference 39

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion High-resolution image synthesis with latent diffusion models

Reference 40

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Komodakis, N

Reference 41

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Deep unsupervised learning using nonequilibrium thermodynamics

Reference 42

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Denoising Diffusion Implicit Models

Reference 43

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Ermon, S

Reference 44

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Ermon, S

Reference 45

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Score-Based Generative Modeling through Stochastic Differential Equations

Reference 46

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Marbach, P

Reference 47

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion and Marbach, P

Reference 48

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion On Evaluation Metrics for Graph Generative Models

Reference 49

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion DiGress: Discrete Denoising diffusion for graph generation

Reference 50

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Graphgan: Graph representation learning with generative adversarial nets

Reference 51

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This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 52

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This paper cites A compact review of molecular property prediction with graph neural networks.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion A compact review of molecular property prediction with graph neural networks

Reference 53

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SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion N., Gomes, J., Geniesse, C., Pappu, A

Reference 54

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This paper cites Exploring randomly wired neural networks for image recognition.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Exploring randomly wired neural networks for image recognition

Reference 55

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This paper cites Poisson flow generative models, 2022.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Poisson flow generative models, 2022

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:43:48.331713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4ac4972b-fe30-451b-ad85-b0911a8d3bdb · outbound

This paper cites Diffsound: Discrete diffusion model for text-to-sound generation.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Diffsound: Discrete diffusion model for text-to-sound generation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:43:48.187818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6282df0d-bd51-4c14-85d9-0ce9bd80a5d2 · outbound

This paper cites Graphrnn: Generating realistic graphs with deep auto-regressive models.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Graphrnn: Generating realistic graphs with deep auto-regressive models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:43:48.048822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d440e090-f460-4f07-ae26-dd6ba85b1e84 · outbound

This paper cites Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T18:43:47.352812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5876be90-f38c-4b11-9770-06f47c4571aa · outbound

This paper cites write newline.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion write newline

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T18:43:47.431645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers