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Clustering graphs using random trees

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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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Dates and versions

hal-02282207 , version 1 (09-09-2019)

Identifiers

  • HAL Id : hal-02282207 , version 1

Cite

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