Hierarchical Graph Clustering using Node Pair Sampling

Thomas Bonald 1, 2 Bertrand Charpentier 1, 2 Alexis Galland 3 Alexandre Hollocou 3, 2
3 DYOGENE - Dynamics of Geometric Networks
Inria de Paris, CNRS - Centre National de la Recherche Scientifique : UMR 8548, DI-ENS - Département d'informatique de l'École normale supérieure
Abstract : We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques. The algorithm is agglomerative and based on a simple distance between clusters induced by the probability of sampling node pairs. We prove that this distance is reducible, which enables the use of the nearest-neighbor chain to speed up the agglomeration. The output of the algorithm is a regular dendrogram, which reveals the multi-scale structure of the graph. The results are illustrated on both synthetic and real datasets.
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Submitted on : Thursday, October 4, 2018 - 12:39:05 PM
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Thomas Bonald, Bertrand Charpentier, Alexis Galland, Alexandre Hollocou. Hierarchical Graph Clustering using Node Pair Sampling. MLG 2018 - 14th International Workshop on Mining and Learning with Graphs, Aug 2018, London, United Kingdom. ⟨hal-01887669⟩



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