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Communication Dans Un Congrès Année : 2018

Hierarchical Graph Clustering using Node Pair Sampling

Résumé

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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Dates et versions

hal-01887669 , version 1 (04-10-2018)

Identifiants

  • HAL Id : hal-01887669 , version 1

Citer

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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