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arXiv · 2406.04860

Multi-View Stochastic Block Models

Vincent Cohen-Addad·Tommaso d'Orsi·Silvio Lattanzi·Rajai Nasser
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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.

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Vincent Cohen-Addad, Tommaso d'Orsi, Silvio Lattanzi, Rajai Nasser. 2024-06-07. Multi-View Stochastic Block Models. https://arxiv.org/abs/2406.04860

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