Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block Models
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Summary
A novel goodness-of-fit test based on the maximum entry of the centered and rescaled adjacency matrix for the stochastic block model, which proves that the null distribution of the test statistic converges in distribution to a Gumbel distribution and shows that both the number of communities and the membership vector can be tested via the proposed method.
- Type
- preprint
- Published
- 2017-03-20
- Cited by
- 54
- References
- 41
- Access
- Open access
- OpenAlex
- https://openalex.org/W2598336484
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:88521284
Keywords
Goodness of fit, Stochastic block model, Test statistic, Null distribution, Gumbel distribution
References
- Likelihood-based model selection for stochastic block models
- On Consistency of Community Detection in Networks
- A Generic Sample Splitting Approach for Refined Community Recovery in Stochastic Block Models
- Role of normalization in spectral clustering for stochastic blockmodels
- How Many Communities Are There?
- Network Cross-Validation for Determining the Number of Communities in Network Data
- Spectral Clustering and Block Models: A Review And A New Algorithm
- A goodness-of-fit test for stochastic block models
- Consistency of community detection in networks under degree-corrected stochastic block models
- Spectral clustering and the high-dimensional stochastic blockmodel
- Pseudo-likelihood methods for community detection in large sparse networks
- Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices
- Matrix estimation by Universal Singular Value Thresholding
- Two moments su ce for Poisson approx-imations: the Chen-Stein method
- FAST COMMUNITY DETECTION BY SCORE
- Consistency of spectral clustering in stochastic block models
- A nonparametric view of network models and Newman–Girvan and other modularities
- The asymptotic distributions of the largest entries of sample correlation matrices
- Stochastic blockmodels: First steps
- Mixed Membership Stochastic Blockmodels
Cited by
- Optimal adaptivity of signed-polygon statistics for network testing
- missSBM: An R Package for Handling Missing Values in the Stochastic Block Model
- A Unified Framework for Tuning Hyperparameters in Clustering Problems
- The asymptotic distribution of modularity in weighted signed networks
- On hyperparameter tuning in general clustering problemsm
- Goodness-of-fit Test for Latent Block Models
- Estimating the number of communities by Stepwise Goodness-of-fit
- Fast Network Community Detection With Profile-Pseudo Likelihood Methods
- A goodness-of-fit test on the number of biclusters in a relational data matrix
- Selective Inference for Latent Block Models
- Test on Stochastic Block Model: Local Smoothing and Extreme Value Theory
- Optimal Estimation of the Number of Network Communities
- The SCORE normalization, especially for highly heterogeneous network and text data
- Estimating the number of communities in the stochastic block model with outliers
- Special invited paper: The SCORE normalization, especially for heterogeneous network and text data
- Profile‐pseudo likelihood methods for community detection of multilayer stochastic block models
- Joint Network Reconstruction and Community Detection from Rich but Noisy Data
- Two-sample test of stochastic block models
- Self-starting monitoring of the progressive type II censoring data based on goodness-of-fit test
- Two-sample test of stochastic block models via the maximum sampling entry-wise deviation
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