# Multi-View Stochastic Block Models

arXiv: https://arxiv.org/abs/2406.04860 · HTML: https://mlsanity.com/paper/2406.04860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Exact Recovery in the Stochastic Block Model](https://mlsanity.com/paper/1405.3267/) (2014)
- [A cost function for similarity-based hierarchical clustering](https://mlsanity.com/paper/1510.05043/) (2015)
- [Community detection thresholds and the weak Ramanujan property](https://mlsanity.com/paper/1311.3085/) (2013)
- [A Proof Of The Block Model Threshold Conjecture](https://mlsanity.com/paper/1311.4115/) (2013)
- [Deep Multimodal Subspace Clustering Networks](https://mlsanity.com/paper/1804.06498/) (2018)
- [Non-backtracking spectrum of random graphs: community detection and non-regular Ramanujan graphs](https://mlsanity.com/paper/1501.06087/) (2015)
- [Consistency Thresholds for the Planted Bisection Model](https://mlsanity.com/paper/1407.1591/) (2014)
- [Belief propagation, robust reconstruction and optimal recovery of block models](https://mlsanity.com/paper/1309.1380/) (2013)
- [Robust recovery for stochastic block models](https://mlsanity.com/paper/2111.08568/) (2021)
- [Information-theoretic thresholds for community detection in sparse networks](https://mlsanity.com/paper/1601.02658/) (2016)
- [Community detection, link prediction, and layer interdependence in multilayer networks](https://mlsanity.com/paper/1701.01369/) (2017)
- [Community detection in multi-relational data with restricted multi-layer stochastic blockmodel](https://mlsanity.com/paper/1506.02699/) (2015)
- [Recovering communities in the general stochastic block model without knowing the parameters](https://mlsanity.com/paper/1506.03729/) (2015)
- [Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions](https://mlsanity.com/paper/2305.10227/) (2023)
- [Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks](https://mlsanity.com/paper/1605.02540/) (2016)
- [On statistical inference when fixed points of belief propagation are unstable](https://mlsanity.com/paper/2101.10882/) (2021)

## Abstract (arXiv)

Graph clustering is a central topic in unsupervised learning with a multitude of practical applications. In recent years, multi-view graph clustering has gained a lot of attention for its applicability to real-world instances where one has access to multiple data sources. In this paper we formalize a new family of models, called textitmulti-view stochastic block models that captures this setting. For this model, we first study efficient algorithms that naively work on the union of multiple graphs. Then, we introduce a new efficient algorithm that provably outperforms previous approaches by analyzing the structure of each graph separately. Furthermore, we complement our results with an information-theoretic lower bound studying the limits of what can be done in this model. Finally, we corroborate our results with experimental evaluations.