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arXiv · 1806.01664

Hierarchical Graph Clustering using Node Pair Sampling

Thomas Bonald·Bertrand Charpentier·Alexis Galland·Alexandre Hollocou
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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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Thomas Bonald, Bertrand Charpentier, Alexis Galland, Alexandre Hollocou. 2018-06-05. Hierarchical Graph Clustering using Node Pair Sampling. https://arxiv.org/abs/1806.01664

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