# Clustering in Partially Labeled Stochastic Block Models via Total Variation Minimization

arXiv: https://arxiv.org/abs/1911.00958 · HTML: https://mlsanity.com/paper/1911.00958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [flow-based clustering and spectral clustering: a comparison](https://mlsanity.com/paper/2206.10019/) (2022)

## Abstract (arXiv)

A main task in data analysis is to organize data points into coherent groups or clusters. The stochastic block model is a probabilistic model for the cluster structure. This model prescribes different probabilities for the presence of edges within a cluster and between different clusters. We assume that the cluster assignments are known for at least one data point in each cluster. In such a partially labeled stochastic block model, clustering amounts to estimating the cluster assignments of the remaining data points. We study total variation minimization as a method for this clustering task. We implement the resulting clustering algorithm as a highly scalable message-passing protocol. We also provide a condition on the model parameters such that total variation minimization allows for accurate clustering.