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
