Multi-View Stochastic Block Models

TL;DR

This paper introduces multi-view stochastic block models for graph clustering, proposing algorithms that outperform previous methods by leveraging multiple data sources, and establishing theoretical limits with experimental validation.

Contribution

It formalizes multi-view stochastic block models, develops improved algorithms, and provides theoretical lower bounds for multi-view graph clustering.

Findings

New algorithms outperform existing methods on multi-view data

Theoretical lower bounds define the limits of clustering accuracy

Experimental results validate the effectiveness of proposed algorithms

Abstract

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 \textit{multi-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.