Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models | Paper Citation Record

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This paper cites A note on the relations between mixture models, maximum- likelihood and entropic optimal transport, 2025.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models A note on the relations between mixture models, maximum- likelihood and entropic optimal transport, 2025

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This paper cites Rohde, and Heiko Hoffmann.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Rohde, and Heiko Hoffmann

Reference 2

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This paper cites Entropic optimal transport is maximum-likelihood deconvolution, 2018.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Entropic optimal transport is maximum-likelihood deconvolution, 2018

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This paper cites Stochastic blockmodels: First steps.Social networks, 5(2):109–137, 1983.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Stochastic blockmodels: First steps.Social networks, 5(2):109–137, 1983

Reference 4

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This paper cites Estimation and prediction for stochastic blockstruc- tures.Journal of the American statistical association, 96(455):1077–1087, 2001.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Estimation and prediction for stochastic blockstruc- tures.Journal of the American statistical association, 96(455):1077–1087, 2001

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This paper cites A mixture model for random graphs.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models A mixture model for random graphs

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This paper cites Network analysis in the social sciences.science, 323(5916):892–895, 2009.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Network analysis in the social sciences.science, 323(5916):892–895, 2009

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This paper cites Journal of Statistical Mechanics: Theory and Experiment2008(10), 10008 (2008) https://doi.org/10.1088/1742-5468/2008/ 10/P10008.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Journal of Statistical Mechanics: Theory and Experiment2008(10), 10008 (2008) https://doi.org/10.1088/1742-5468/2008/ 10/P10008

Reference 8

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This paper cites Community detection in graphs.Physics reports, 486(3-5):75–174, 2010.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Community detection in graphs.Physics reports, 486(3-5):75–174, 2010

Reference 9

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This paper cites A tutorial on spectral clustering.Statistics and computing, 17(4):395–416, 2007.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models A tutorial on spectral clustering.Statistics and computing, 17(4):395–416, 2007

Reference 10

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This paper cites Consistency of spectr al clustering in stochastic block models.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Consistency of spectr al clustering in stochastic block models

Reference 11

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This paper cites Mean-field theory of graph neural networks in graph partitioning.Advances in neural information processing systems, 31, 2018.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Mean-field theory of graph neural networks in graph partitioning.Advances in neural information processing systems, 31, 2018

Reference 12

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This paper cites Neurocut: A neural approach for robust graph partitioning.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Neurocut: A neural approach for robust graph partitioning

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This paper cites Scalable gromov-wasserstein learning for graph partitioning and matching.Advances in neural information processing systems, 32, 2019.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Scalable gromov-wasserstein learning for graph partitioning and matching.Advances in neural information processing systems, 32, 2019

Reference 14

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This paper cites Semi-relaxed Gromov-Wasserstein divergence with applications on graphs.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Semi-relaxed Gromov-Wasserstein divergence with applications on graphs

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This paper cites Heat kernel based community detection.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Heat kernel based community detection

Reference 16

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This paper cites Stochastic blockmodels and community structure in networks.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 83(1):016107, 2011.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Stochastic blockmodels and community structure in networks.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 83(1):016107, 2011

Reference 17

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This paper cites Assessing a mixture model for clustering with the integrated completed likelihood.IEEE transactions on pattern analysis and machine intelligence, 22(7):719–725, 2000.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Assessing a mixture model for clustering with the integrated completed likelihood.IEEE transactions on pattern analysis and machine intelligence, 22(7):719–725, 2000

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This paper cites Improved bayesian inference for the stochastic block model with application to large networks.Computational Statistics & Data Analysis, 60:12–31, 2013.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Improved bayesian inference for the stochastic block model with application to large networks.Computational Statistics & Data Analysis, 60:12–31, 2013

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This paper cites Hierarchical clustering with discrete latent variable models and the integrated classification likelihood.Advances in Data Analysis and Classification, 15(4):957–986, 2021.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Hierarchical clustering with discrete latent variable models and the integrated classification likelihood.Advances in Data Analysis and Classification, 15(4):957–986, 2021

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This paper cites Annals of Statistics43(6), 2624– 2652 (2015) https://doi.org/10.1214/15-AOS1354.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Annals of Statistics43(6), 2624– 2652 (2015) https://doi.org/10.1214/15-AOS1354

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This paper cites Maximum likelihood estimation of sparse networks with missing observations.Journal of Statistical Planning and Inference, 215:299–329, 2021.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Maximum likelihood estimation of sparse networks with missing observations.Journal of Statistical Planning and Inference, 215:299–329, 2021

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Optimality of variational inference for stochasticblock model with missing links

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This paper cites Spectral clustering of graphs with the bethe hessian, 2014.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Spectral clustering of graphs with the bethe hessian, 2014

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This paper cites Recovering communities in the general stochastic block model without knowing the parameters, 2015.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Recovering communities in the general stochastic block model without knowing the parameters, 2015

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This paper cites Grundlehren der mathematis- chen Wissenschaften.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Grundlehren der mathematis- chen Wissenschaften

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This paper cites Computational optimal transport, 2020.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Computational optimal transport, 2020

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This paper cites A survey on optimal transport for machine learning: Theory and applications.IEEE Access, 2025.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models A survey on optimal transport for machine learning: Theory and applications.IEEE Access, 2025

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Gromov—wasserstein distances and the metric approach to object matching

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models American Mathematical Society, 2023

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This paper cites The gromov–wasserstein distance between networks and stable network invariants.Information and Inference: A Journal of the IMA, 8(4):757–787, 2019.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models The gromov–wasserstein distance between networks and stable network invariants.Information and Inference: A Journal of the IMA, 8(4):757–787, 2019

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Gromov-wasserstein learning for graph matching and node embedding

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Quantized gromov-wasserstein

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Gromov-Wasserstein Averaging of Kernel and Distance Matrices

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Optimal transport for structured data with application on graphs

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Generalized spectral clustering via gromov-wasserstein learning

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Optimal transport-based cluster- ing of attributed graphs with an application to road traffic data, 2025

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Learning graphons via struc- tured gromov-wasserstein barycenters

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models A gromov-wasserstein geometric view of spectrum-preserving graph coarsening

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Gromov-wasserstein factorization models for graph clustering

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Online graph dictionary learning

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Robust graph dictionary learning

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Generative graph dictionary learning

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This paper cites Template based graph neural network with optimal transport distances.Advances in Neural Information Processing Systems, 35:11800–11814, 2022.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Template based graph neural network with optimal transport distances.Advances in Neural Information Processing Systems, 35:11800–11814, 2022

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Wasserstein barycenter matching for graph size generalization of message passing neural networks

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Reimagining graph classification from a prototype view with optimal transport: Algorithm and theorem

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models The quest for the GRAph level autoencoder (GRALE)

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Distributional reduction: Unifying dimensionality reduction and clustering with gromov-wasserstein.Transactions on Machine Learning Research Journal, 2025

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Generalized dimension reduction using semi-relaxed gromov-wasserstein distance

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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Kernel k-means: spectral clustering and normalized cuts

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This paper cites A review of stochastic block models and extensions for graph clustering.Applied Network Science, 4(1):122, 2019.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models A review of stochastic block models and extensions for graph clustering.Applied Network Science, 4(1):122, 2019

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This paper cites Daudin, and Laurent Pierre.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Daudin, and Laurent Pierre

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This paper cites Convergence of the groups posterior distribution in latent or stochastic block models.Bernoulli, 21(1):537–573, 2015.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Convergence of the groups posterior distribution in latent or stochastic block models.Bernoulli, 21(1):537–573, 2015

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This paper cites A graph matching approach to balanced data sub- sampling for self-supervised learning.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models A graph matching approach to balanced data sub- sampling for self-supervised learning

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Observation d0727094-917c-429d-9975-0570b93bc630 · outbound

This paper cites Itera- tive bregman projections for regularized transportation problems.SIAM Journal on Scientific Computing, 37(2):A1111–A1138, 2015.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Itera- tive bregman projections for regularized transportation problems.SIAM Journal on Scientific Computing, 37(2):A1111–A1138, 2015

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Observation 24ebd4c9-4661-4d62-8269-55af54ef06bb · outbound

This paper cites The map equation.The European Physical Journal Special Topics, 178(1):13–23, 2009.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models The map equation.The European Physical Journal Special Topics, 178(1):13–23, 2009

Reference 56

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Observation e44a24e1-0b8f-4d7d-aa16-f7566c44c500 · outbound

This paper cites Blockmodels: A r-package for estimating in latent block model and stochastic block model, with various probability functions, with or without covariates, 2016.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Blockmodels: A r-package for estimating in latent block model and stochastic block model, with various probability functions, with or without covariates, 2016

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Observation 7bd63b47-1529-4a47-880c-b41b8a2fe667 · outbound

This paper cites Adjusting for chance clustering comparison measures.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Adjusting for chance clustering comparison measures

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This paper cites Comparing partitions.J.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Comparing partitions.J

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This paper cites Cambridge Series in Statistical and Probabilistic Mathematics.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Cambridge Series in Statistical and Probabilistic Mathematics

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Observation 1ae6b539-9938-4acd-88f1-c9ed1f5c5fbf · outbound

This paper cites Asymptotic statistics (1998).

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Asymptotic statistics (1998)

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This paper cites Pot python optimal transport (version 0.9.5), 2024.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Pot python optimal transport (version 0.9.5), 2024

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This paper cites an unresolved cited work.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Unresolved cited work

Reference 63

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Observation fc3cd6aa-2499-4ebb-9a5c-2713133ce28e · outbound

This paper cites projection.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models projection

Reference 64

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Observation 0096bcca-eaae-4142-9e60-0a216b8a9712 · outbound

This paper cites Justification: The research does not involve human subjects, therefore IRB approval is not applicable.

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models Justification: The research does not involve human subjects, therefore IRB approval is not applicable

Reference 65

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