# Nonparametric weighted stochastic block models

arXiv: https://arxiv.org/abs/1708.01432 · HTML: https://mlsanity.com/paper/1708.01432/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2017.

## Built on by (most cited first, 10 of 10)

- [Community detection for weighted bipartite networks](https://mlsanity.com/paper/2109.10319/) (2021)
- [Restricted Tweedie Stochastic Block Models](https://mlsanity.com/paper/2310.10952/) (2023)
- [Computational limits to the legibility of the imaged human brain](https://mlsanity.com/paper/2309.07096/) (2023)
- [MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length](https://mlsanity.com/paper/2409.10263/) (2024)
- [Community Detection in Weighted Multilayer Networks with Ambient Noise](https://mlsanity.com/paper/2103.00486/) (2021)
- [Block Dense Weighted Networks with Augmented Degree Correction](https://mlsanity.com/paper/2105.12290/) (2021)
- [The minimal computational substrate of fluid intelligence](https://mlsanity.com/paper/2308.07039/) (2023)
- [Learning dynamic representations of the functional connectome in neurobiological networks](https://mlsanity.com/paper/2402.14102/) (2024)
- [Signed graphs in data sciences via communicability geometry](https://mlsanity.com/paper/2403.07493/) (2024)
- [Why the Metric Backbone Preserves Community Structure](https://mlsanity.com/paper/2406.03852/) (2024)

## Abstract (arXiv)

We present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization. Our method is nonparametric, and thus does not require the prior knowledge of the number of groups or other dimensions of the model, which are instead inferred from data. We give a comprehensive treatment of different kinds of edge weights (i.e. continuous or discrete, signed or unsigned, bounded or unbounded), as well as arbitrary weight transformations, and describe an unsupervised model selection approach to choose the best network description. We illustrate the application of our method to a variety of empirical weighted networks, such as global migrations, voting patterns in congress, and neural connections in the human brain.