Graph Community Augmentation with GMM-based Modeling in Latent Space | Paper Citation Record

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This paper cites Deep gen- erative modelling: a comparative review of V AEs, GANs, normalizing flows, energy-based and autoregressive models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Deep gen- erative modelling: a comparative review of V AEs, GANs, normalizing flows, energy-based and autoregressive models,

Reference 1

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Observation 2795815d-5517-4474-b0f3-b2b9e6fecf30 · outbound

This paper cites Graph generators: state of the art and open challenges,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Graph generators: state of the art and open challenges,

Reference 2

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This paper cites Deep graph generators: a survey,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Deep graph generators: a survey,

Reference 3

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This paper cites A systematic survey on deep generative models for graph generation,.

Graph Community Augmentation with GMM-based Modeling in Latent Space A systematic survey on deep generative models for graph generation,

Reference 4

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This paper cites A survey on deep graph generation: methods and applications,.

Graph Community Augmentation with GMM-based Modeling in Latent Space A survey on deep graph generation: methods and applications,

Reference 5

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Observation bf7e10e0-d04b-494a-9295-bfe3792b8e90 · outbound

This paper cites Data augmentation for deep graph learning: a survey,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Data augmentation for deep graph learning: a survey,

Reference 6

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Observation 89ecf537-205f-401f-bb0c-f231d0d0dccf · outbound

This paper cites Graph data augmentation for graph machine learning: a survey,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Graph data augmentation for graph machine learning: a survey,

Reference 7

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This paper cites Keygraph: automatic index- ing by co-occurrence graph based on building construction metaphor,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Keygraph: automatic index- ing by co-occurrence graph based on building construction metaphor,

Reference 8

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This paper cites Chance discoveries for making decisions in complex real world,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Chance discoveries for making decisions in complex real world,

Reference 9

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Graph Community Augmentation with GMM-based Modeling in Latent Space Unresolved cited work

Reference 10

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Observation 8f7333a9-b6db-4643-9c40-a30b4490dd5a · outbound

This paper cites Learning community embedding with community detection and node embedding on graphs,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Learning community embedding with community detection and node embedding on graphs,

Reference 11

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Observation 318a420d-c7d9-4a68-9c30-58467be43d63 · outbound

This paper cites Deep clustering by Gaussian mixture variational autoencoders with graph embedding,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Deep clustering by Gaussian mixture variational autoencoders with graph embedding,

Reference 12

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Observation ddc65555-1892-493c-8d9e-9da93acbbc62 · outbound

This paper cites Principled knowledge extrapolation with GANs,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Principled knowledge extrapolation with GANs,

Reference 13

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Observation f2d54340-cea1-41d1-a6b3-6ba291ef4e96 · outbound

This paper cites Deep extrapolation for attribute-enhanced generation,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Deep extrapolation for attribute-enhanced generation,

Reference 14

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This paper cites A theory of independent mechanisms for extrapolation in generative models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space A theory of independent mechanisms for extrapolation in generative models,

Reference 15

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This paper cites Controllable and progressive image extrapolation,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Controllable and progressive image extrapolation,

Reference 16

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This paper cites A deep generative approach to search extrapolation and recommendation,.

Graph Community Augmentation with GMM-based Modeling in Latent Space A deep generative approach to search extrapolation and recommendation,

Reference 17

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Observation cbdf27d6-d56e-46a9-977c-2c83f1af81c0 · outbound

This paper cites Half-Hop: a graph upsampling approach for slowing down message passing,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Half-Hop: a graph upsampling approach for slowing down message passing,

Reference 18

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Observation bfbca477-5081-4597-8f84-3629ea062b0e · outbound

This paper cites Local augmentation for graph neural networks,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Local augmentation for graph neural networks,

Reference 19

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Observation b56e31ce-4fb1-4101-b272-6c59aa003760 · outbound

This paper cites GMMDA: Gaussian mixture modeling of graph in latent space for graph data augmentation,.

Graph Community Augmentation with GMM-based Modeling in Latent Space GMMDA: Gaussian mixture modeling of graph in latent space for graph data augmentation,

Reference 20

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Observation 563386ca-f596-46ad-84ac-4b017e46bb23 · outbound

This paper cites Spectral augmentation for self-supervised learning on graphs,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Spectral augmentation for self-supervised learning on graphs,

Reference 21

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This paper cites Graph self-supervised learning with accurate discrepancy learning,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Graph self-supervised learning with accurate discrepancy learning,

Reference 22

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Observation 77b5a6cf-e9f2-4094-a52c-49aee6feebd6 · outbound

This paper cites Out-Of-Distribution Generalization on Graphs: A Survey.

Graph Community Augmentation with GMM-based Modeling in Latent Space Out-Of-Distribution Generalization on Graphs: A Survey

Reference 23

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This paper cites Causal- GAN: Learning causal implicit generative models with adversarial training,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Causal- GAN: Learning causal implicit generative models with adversarial training,

Reference 24

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This paper cites Counterfactual generative networks,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Counterfactual generative networks,

Reference 25

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This paper cites CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets.

Graph Community Augmentation with GMM-based Modeling in Latent Space CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets

Reference 26

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Observation 8ee92bff-d6ef-4c08-8449-7e5e88f590d8 · outbound

This paper cites CausalV AE: disentangled representation learning via neural structural causal models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space CausalV AE: disentangled representation learning via neural structural causal models,

Reference 27

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Observation bf605eed-2538-4d7a-a0e0-8a3c3404a490 · outbound

This paper cites The counterfactual χ-GAN: finding comparable cohorts in observational health data,.

Graph Community Augmentation with GMM-based Modeling in Latent Space The counterfactual χ-GAN: finding comparable cohorts in observational health data,

Reference 28

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This paper cites Designing counterfactual generators using deep model inversion,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Designing counterfactual generators using deep model inversion,

Reference 29

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This paper cites Causal inference in statistics: An overview,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Causal inference in statistics: An overview,

Reference 30

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Graph Community Augmentation with GMM-based Modeling in Latent Space Unresolved cited work

Reference 31

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This paper cites Deep learning on graphs: a survey,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Deep learning on graphs: a survey,

Reference 32

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This paper cites Variational Graph Auto-Encoders.

Graph Community Augmentation with GMM-based Modeling in Latent Space Variational Graph Auto-Encoders

Reference 33

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This paper cites Modeling by shortest data description,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Modeling by shortest data description,

Reference 34

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This paper cites Yamanishi, Learning with the Minimum Description Length Princi- ple.

Graph Community Augmentation with GMM-based Modeling in Latent Space Yamanishi, Learning with the Minimum Description Length Princi- ple

Reference 35

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This paper cites Decomposed normalized maximum likelihood codelength criterion for selecting hierarchical latent variable models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Decomposed normalized maximum likelihood codelength criterion for selecting hierarchical latent variable models,

Reference 36

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This paper cites The decomposed normalized maximum likelihood code-length criterion for selecting hier- archical latent variable models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space The decomposed normalized maximum likelihood code-length criterion for selecting hier- archical latent variable models,

Reference 37

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Observation 42fc6619-a9b3-4060-b2d8-6fcfca81a79e · outbound

This paper cites Graph summarization with latent variable probabilistic models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Graph summarization with latent variable probabilistic models,

Reference 38

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This paper cites Detecting hierarchical changes in latent variable model,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Detecting hierarchical changes in latent variable model,

Reference 39

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This paper cites Balancing summarization and change detection in graph streams,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Balancing summarization and change detection in graph streams,

Reference 40

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Graph Community Augmentation with GMM-based Modeling in Latent Space Rissanen, Optimal estimation of parameters

Reference 41

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This paper cites A linear-time algorithm for computing the multinomial stochastic complexity,.

Graph Community Augmentation with GMM-based Modeling in Latent Space A linear-time algorithm for computing the multinomial stochastic complexity,

Reference 42

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This paper cites Approximating the Kullback Leibler divergence between Gaussian mixture models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Approximating the Kullback Leibler divergence between Gaussian mixture models,

Reference 43

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This paper cites Lower and upper bounds for approximation of the Kullback-Leibler divergence between Gaussian mixture models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Lower and upper bounds for approximation of the Kullback-Leibler divergence between Gaussian mixture models,

Reference 44

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Observation 48847a0c-f69f-4fb2-b7e5-1f8ac32babf4 · outbound

This paper cites Matrix exponentiated gradient updates for on-line learning and Bregman projection,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Matrix exponentiated gradient updates for on-line learning and Bregman projection,

Reference 45

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This paper cites Exponentiated gradient versus gradient descent for linear predictors,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Exponentiated gradient versus gradient descent for linear predictors,

Reference 46

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Observation c6ca03e3-1cea-4f01-94f5-0cbfb82d66d5 · outbound

This paper cites SPECTRE: spectral conditioning helps to overcome the expressivity limits of one- shot graph generators,.

Graph Community Augmentation with GMM-based Modeling in Latent Space SPECTRE: spectral conditioning helps to overcome the expressivity limits of one- shot graph generators,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:48.161365Z

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Observation e3e3170a-fdd6-43b7-82c0-b1674ae25f56 · outbound

This paper cites Graph generation with diffusion mixture,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Graph generation with diffusion mixture,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:48.137667Z

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Observation f156db60-2b7f-4c00-85d5-6627a26a8e79 · outbound

This paper cites Estimation and prediction for stochastic blockmodels for graphs with latent block structure,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Estimation and prediction for stochastic blockmodels for graphs with latent block structure,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:48.115220Z

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Observation 45680154-fd83-485c-9c26-43eeb307a472 · outbound

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

Graph Community Augmentation with GMM-based Modeling in Latent Space Score-based generative modeling of graphs via the system of stochastic differential equations,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:48.089357Z

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Observation 3b5233ba-3e7d-4916-9ef0-070d8eac83a8 · outbound

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

Graph Community Augmentation with GMM-based Modeling in Latent Space Digress: discrete denoising diffusion for graph generation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:48.064103Z

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Observation 8da239d9-080d-4291-8bda-3e58b3263096 · outbound

This paper cites Denoising diffusion probabilistic models,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Denoising diffusion probabilistic models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:48.043340Z

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Observation e81c335b-8a1d-4730-957d-0b9e12aa616b · outbound

This paper cites Visualizing data using t-sne,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Visualizing data using t-sne,

Reference 53

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

Graph Community Augmentation with GMM-based Modeling in Latent Space Graphrnn: generating realistic graphs with deep auto-regressive models,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:48.011902Z

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Observation c0981538-538b-48b6-a80e-e4099a4f99bc · outbound

This paper cites Efficient and degree-guided graph generation via discrete diffusion modeling,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Efficient and degree-guided graph generation via discrete diffusion modeling,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:47.991030Z

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Observation 61b26fa6-44da-49a7-b9fe-f5cb9c8599ad · outbound

This paper cites Diffusing gaussian mixtures for generating categorical data,.

Graph Community Augmentation with GMM-based Modeling in Latent Space Diffusing gaussian mixtures for generating categorical data,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:42:47.974591Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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