A Bayesian Approach To Graph Partitioning
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Abstract (arXiv)
A new algorithm based on bayesian inference for learning local graph conductance based on Gaussian Process(GP) is given that uses advanced MCMC convergence ideas to create a scalable and fast algorithm for convergence to stationary distribution which is provided to learn the bahavior of conductance when traversing the indirected weighted graph. First metric embedding is used to represent the vertices of the graph. Then, uniform induced conductance is calculated for training points. Finally, in the learning step, a gaussian process is used to approximate the uniform induced conductance. MCMC is used to measure uncertainty of estimated hyper-parameters.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 24 Apr 2022.
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11 of 33 references.
References · 11
- 2015MCMC for Variationally Sparse Gaussian Processescited by 44
- 2019Optimality of Spectral Clustering in the Gaussian Mixture Modelcited by 42
- 2019Approximate Inference for Fully Bayesian Gaussian Process Regressioncited by 31
- 2013Recent Advances in Graph Partitioningcited by 24
- 2014Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximationscited by 17
- 2013Flow-Based Algorithms for Local Graph Clusteringcited by 8
- 2015Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlocited by 6
- 2014Warped Mixtures for Nonparametric Cluster Shapescited by 3
- 2019Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processescited by 1
- 2021On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approachcited by 1
- 2022Finding Many Sparse Cuts Using Entropy Maximizationcited by 1
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