# Global and Local Information in Clustering Labeled Block Models

arXiv: https://arxiv.org/abs/1404.6325 · HTML: https://mlsanity.com/paper/1404.6325/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2014.

## Built on by (most cited first, 5 of 5)

- [Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities](https://mlsanity.com/paper/2107.06767/) (2021)
- [Exact Community Recovery in Correlated Stochastic Block Models](https://mlsanity.com/paper/2203.15736/) (2022)
- [Streaming Belief Propagation for Community Detection](https://mlsanity.com/paper/2106.04805/) (2021)
- [Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold](https://mlsanity.com/paper/2205.11677/) (2022)
- [The Value of Depth in Message Passing on Sparse Graphs: A Kesten-Stigum Dichotomy](https://mlsanity.com/paper/2607.16676/) (2026)

## Abstract (arXiv)

The stochastic block model is a classical cluster-exhibiting random graph model that has been widely studied in statistics, physics and computer science. In its simplest form, the model is a random graph with two equal-sized clusters, with intra-cluster edge probability p, and inter-cluster edge probability q. We focus on the sparse case, i.e., p, q = O(1/n), which is practically more relevant and also mathematically more challenging. A conjecture of Decelle, Krzakala, Moore and Zdeborova, based on ideas from statistical physics, predicted a specific threshold for clustering. The negative direction of the conjecture was proved by Mossel, Neeman and Sly (2012), and more recently the positive direction was proven independently by Massoulie and Mossel, Neeman, and Sly. In many real network clustering problems, nodes contain information as well. We study the interplay between node and network information in clustering by studying a labeled block model, where in addition to the edge information, the true cluster labels of a small fraction of the nodes are revealed. In the case of two clusters, we show that below the threshold, a small amount of node information does not affect recovery. On the other hand, we show that for any small amount of information efficient local clustering is achievable as long as the number of clusters is sufficiently large (as a function of the amount of revealed information).