Model Selection for Degree-corrected Block Models

@article{Yan2014ModelSF,
  title={Model Selection for Degree-corrected Block Models},
  author={Xiaoran Yan and Jacob E. Jensen and F. Krzakala and C. Moore and C. Shalizi and L. Zdeborov{\'a} and P. Zhang and Yaojia Zhu},
  journal={Journal of statistical mechanics},
  year={2014},
  volume={2014 5}
}
  • Xiaoran Yan, Jacob E. Jensen, +5 authors Yaojia Zhu
  • Published 2014
  • Computer Science, Medicine, Physics, Mathematics
  • Journal of statistical mechanics
  • The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks. We illustrate these challenges, and show one way to resolve them, by considering the key network-analysis problem of dividing a graph into communities or blocks of nodes with… CONTINUE READING
    90 Citations
    Infinite-degree-corrected stochastic block model.
    • 17
    • PDF
    Bayesian model selection of stochastic block models
    • Xiaoran Yan
    • Mathematics, Computer Science
    • 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
    • 2016
    • 22
    • PDF
    Testing Degree Corrections in Stochastic Block Models
    • 1
    • PDF
    Network Cross-Validation for Determining the Number of Communities in Network Data
    • 96
    • PDF
    Adjusted chi-square test for degree-corrected block models
    • PDF
    Hierarchical block structures and high-resolution model selection in large networks
    • 217
    • PDF
    Learning Latent Block Structure in Weighted Networks
    • 173
    • Highly Influenced
    • PDF

    References

    SHOWING 1-10 OF 66 REFERENCES
    Oriented and degree-generated block models: generating and inferring communities with inhomogeneous degree distributions
    • 28
    • PDF
    Null models for network data
    • 55
    • PDF
    Stochastic blockmodels and community structure in networks
    • B. Karrer, M. Newman
    • Mathematics, Computer Science
    • Physical review. E, Statistical, nonlinear, and soft matter physics
    • 2011
    • 1,280
    • Highly Influential
    • PDF
    Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
    • 570
    • PDF
    Stochastic Block Models of Mixed Membership
    • 55
    • PDF
    Stochastic blockmodels: First steps
    • 1,636
    • Highly Influential
    • PDF
    Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure
    • 506
    Learning latent structure in complex networks
    • 11
    • PDF
    Hierarchical structure and the prediction of missing links in networks
    • 1,639
    • PDF