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Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs

Kevin Dalleau 1 Miguel Couceiro 2 Malika Smail-Tabbone 1 
1 CAPSID - Computational Algorithms for Protein Structures and Interactions
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : A current challenge in graph clustering is to tackle the issue of complex networks, i.e, graphs with attributed vertices and/or edges. In this paper, we present GraphTrees, a novel method that relies on random decision trees to compute pairwise dissimilarities between vertices in a graph. We show that using different types of trees, it is possible to extend this framework to graphs where the vertices have attributes. While many existing methods that tackle the problem of clustering vertices in an attributed graph are limited to categorical attributes, GraphTrees can handle heterogeneous types of vertex attributes. Moreover, unlike other approaches, the attributes do not need to be preprocessed. We also show that our approach is competitive with well-known methods in the case of non-attributed graphs in terms of quality of clustering, and provides promising results in the case of vertex-attributed graphs. By extending the use of an already well established approach-the random trees-to graphs, our proposed approach opens new research directions, by lever-aging decades of research on this topic.
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Preprints, Working Papers, ...
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Submitted on : Friday, January 3, 2020 - 5:24:41 PM
Last modification on : Thursday, August 4, 2022 - 5:18:49 PM
Long-term archiving on: : Monday, April 6, 2020 - 8:26:37 PM


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  • HAL Id : hal-02427563, version 1


Kevin Dalleau, Miguel Couceiro, Malika Smail-Tabbone. Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs. 2019. ⟨hal-02427563⟩



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