Cookbook guides

Contents:

• Inferring modular network structure
• Background: Nonparametric statistical inference
  • Minimum description length (MDL)
• The stochastic block model (SBM)
  • The nested stochastic block model
• Inferring the best partition
  • Hierarchical partitions
  • Refinements using merge-split MCMC
• Model selection
• Sampling from the posterior distribution
  • Hierarchical partitions
  • Characterizing the posterior distribution
• Model class selection
• Edge weights and covariates
  • Model selection
  • Posterior sampling
• Layered networks
• Assortative community structure
• Ordered community structure
• References
• Uncertain network reconstruction
  • Measured networks
    • Heterogeneous errors
  • Extraneous error estimates
  • Latent Poisson multigraphs
  • Latent triadic closures
• Edge prediction as binary classification
• References
• Network reconstruction from dynamics and behavior
  • Reconstruction with synthetic data
    • \(L_1\) regularization
  • Reconstruction with empirical data
  • References
• Animations with graph-tool
  • Simple interactive animations
  • SIRS epidemics
  • Dynamic layout
  • Interactive visualizations
• Integration with matplotlib
  • Integration with basemap
• Writing extensions in C++
  • Range-based iteration over vertices and edges
  • Extracting specific property maps
  • Checked and unchecked property maps
  • References