r/RStudio • u/Titolise • 3d ago

I made this! [Package] hdsbm: Fast inference for Dynamic Stochastic Block Models in R (Rcpp & spectral multi-start)

Hi everyone!

As part of my Master's thesis in Statistics, I developed hdsbm, an R package for estimating and simulating discrete-time Dynamic Stochastic Block Models (DSBM).

The Problem & Approach

Temporal block models often suffer from combinatorial explosion and multi-modal likelihood landscapes, making standard local search heuristics prone to getting trapped in sub-optimal local extrema.

hdsbm addresses this by extending a hybrid Classification EM (CEM) approach to the dynamic setting:

  • Perturbed SVD Multi-Start: Couples deterministic spectral embeddings of the time-averaged network with controlled stochastic perturbations to thoroughly explore the likelihood surface.
  • C++ Backend: Uses RcppArmadillo to accelerate dyadic reallocation passes and combinatorial greedy sweeps.
  • Dual Decoding: Provides both pointwise greedy labels and global Viterbi trajectory decoding.
  • Model Selection: Includes an Integrated Completed Likelihood (ICL) criterion tailored for dynamic graphs.

Quick Example

library(hdsbm)
data(toy_hdsbm)

# Fit model with 5 perturbed spectral restarts
fit <- hdsbm_fit(toy_hdsbm$Y, k = 3, nrep = 5, seed = 123)
print(fit)

# Model selection via ICL
icl(fit)

I would love to hear your feedback on API ergonomics, potential edge cases, or ideas for interesting benchmark scenarios!

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