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Clustering graphs using random trees

Kevin Dalleau 1, 2 Miguel Couceiro 3 Malika Smaïl-Tabbone 3
2 CAPSID - Computational Algorithms for Protein Structures and Interactions
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
3 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this work-in-progress paper, we present GraphTrees, a novel method that relies on random decision trees to compute pairwise distances between vertices in a graph. We show that our approach is competitive with the state of the art methods in the case of non-attributed graphs in terms of quality of clustering. By extending the use of an already ubiquitous approach-the random trees-to graphs, our proposed approach opens new research directions, by leveraging decades of research on this topic.
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Preprints, Working Papers, ...
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Submitted on : Monday, September 9, 2019 - 6:38:20 PM
Last modification on : Wednesday, November 3, 2021 - 4:47:37 AM
Long-term archiving on: : Saturday, February 8, 2020 - 12:00:25 AM


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  • HAL Id : hal-02282207, version 1


Kevin Dalleau, Miguel Couceiro, Malika Smaïl-Tabbone. Clustering graphs using random trees. 2019. ⟨hal-02282207⟩



Les métriques sont temporairement indisponibles