How Many Communities Are There?
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Summary
This work proposes composite likelihood BIC (CL-BIC) to select the number of communities, and shows it is robust against possible misspecifications in the underlying stochastic blockmodel assumptions.
- Type
- article
- Published
- 2014-12-04
- Cited by
- 126
- References
- 47
- Access
- Open access
- OpenAlex
- https://openalex.org/W1820369233
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:12684326
Keywords
Bayesian information criterion, Computer science, Conditional independence, Disjoint sets, Cluster analysis
References
- Model‐based clustering for social networks
- Estimating a Dirichlet distribution
- Bayesian analysis of mixture models with an unknown number of components- an alternative to reversible jump methods
- On Consistency of Community Detection in Networks
- On the generation of correlated artificial binary data
- On the bayes-optimality of F-measure maximizers
- Network Cross-Validation for Determining the Number of Communities in Network Data
- Null models for network data
- AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS
- Consistency of community detection in networks under degree-corrected stochastic block models
- Spectral clustering and the high-dimensional stochastic blockmodel
- Bayesian finite mixtures with an unknown number of components: The allocation sampler
- Computational Statistical Methods for Social Network Models
- Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
- Local dependence in random graph models: characterization, properties and statistical inference
- Random Fields on a Network: Modeling, Statistics, and Applications
- Particle filters for mixture models with an unknown number of components
- FAST COMMUNITY DETECTION BY SCORE
- A Survey of Statistical Network Models
- Objective Criteria for the Evaluation of Clustering Methods
Cited by
- Estimating the number of communities in networks by spectral methods
- Likelihood-based model selection for stochastic block models
- Network Cross-Validation for Determining the Number of Communities in Network Data
- A goodness-of-fit test for stochastic block models
- Problems in Network Modeling: Estimating Edges and Community Detection
- Advances in Model Selection Techniques with Applications to Statistical Network Analysis and Recommender Systems
- Bayesian Community Detection
- Community detection with nodal information: Likelihood and its variational approximation
- On consistency of model selection for stochastic block models
- Rejoinder: “Coauthorship and citation networks for statisticians”
- Estimating Community Structure in Networks by Spectral Methods
- Generalizations and Applications of the Stochastic Block Model to Basketball Games and Variable Selection Problems
- Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block Models
- Massive-scale estimation of exponential-family random graph models with local dependence
- A new SVD approach to optimal topic estimation
- Optimal hypothesis testing for stochastic block models with growing degrees
- A survey on theoretical advances of community detection in networks
- Estimating network memberships by simplex vertex hunting
- Model-based clustering of time-evolving networks through temporal exponential-family random graph models
- Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis
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