mlsanityData to 7 Oct 2026

Bayesian stochastic blockmodeling

arXiv 1705.10225 ↗First posted 29 May 2017stat.ML cond-mat.stat-mech physics.data-ancited by 27

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Abstract (arXiv)

This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic blockmodel (SBM), as well as its degree-corrected and overlapping generalizations. We focus on nonparametric formulations that allow their inference in a manner that prevents overfitting, and enables model selection. We discuss aspects of the choice of priors, in particular how to avoid underfitting via increased Bayesian hierarchies, and we contrast the task of sampling network partitions from the posterior distribution with finding the single point estimate that maximizes it, while describing efficient algorithms to perform either one. We also show how inferring the SBM can be used to predict missing and spurious links, and shed light on the fundamental limitations of the detectability of modular structures in networks.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2017.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 2; 2024: 3; 2025: 5; 2026: 3.

27 papers.

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  1. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 96
  2. 2021Generative hypergraph clustering: from blockmodels to modularitycited by 48
  3. 2022Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysiscited by 12
  4. 2021Partition and Code: learning how to compress graphscited by 10
  5. 2021Disentangling homophily, community structure and triadic closure in networkscited by 6
  6. 2022Estimating Social Influence from Observational Datacited by 5
  7. 2024Network reconstruction via the minimum description length principlecited by 5
  8. 2023Neural-prior stochastic block modelcited by 4
  9. 2021Multilayer Networks for Text Analysis with Multiple Data Typescited by 3
  10. 2021Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truthscited by 3
  11. 2022Implicit models, latent compression, intrinsic biases, and cheap lunches in community detectioncited by 3
  12. 2025Estimation of partial rankings from sparse, noisy comparisonscited by 3
15 more
  1. 2025RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Modelscited by 3
  2. 2021Semi-Supervised Clustering with Inaccurate Pairwise Annotationscited by 2
  3. 2022Systematic assessment of the quality of fit of the stochastic block model for empirical networkscited by 1
  4. 2022Consistency between ordering and clustering methods for graphscited by 1
  5. 2024Symmetry-driven embedding of networks in hyperbolic spacecited by 1
  6. 2025Deep Graph Learning will stall without Network Sciencecited by 1
  7. 2021Robustness modularity in complex networkscited by 0
  8. 2021An adaptation of InfoMap to absorbing random walks using absorption-scaled graphscited by 0
  9. 2023A Simple Quantum Blockmodeling with Qubits and Permutationscited by 0
  10. 2024Learning dynamic representations of the functional connectome in neurobiological networkscited by 0
  11. 2025Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Modelscited by 0
  12. 2025Large Scale Community-Aware Network Generationcited by 0
  13. 2026Graph energy as a measure of community detectability in networkscited by 0
  14. 2026Scalable inference of spatial regions and temporal signatures from time seriescited by 0
  15. 2026Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraphcited by 0