Summary of Multi-view Stochastic Block Models, by Vincent Cohen-addad et al.
Multi-View Stochastic Block Models
by Vincent Cohen-Addad, Tommaso d’Orsi, Silvio Lattanzi, Rajai Nasser
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel family of graph clustering models, specifically designed for multi-view settings where multiple data sources are available. The proposed multi-view stochastic block models (MSBM) formalize the problem of clustering graphs in the presence of multiple views. This is particularly relevant to real-world applications where one has access to various data sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new type of graph clustering, perfect for situations where you have multiple types of information about people or things. It’s like trying to group friends based on their favorite foods, hobbies, and music preferences. The authors created a special kind of model that can handle this multi-view data and cluster the graphs accordingly. |
Keywords
* Artificial intelligence * Clustering
