Hierarchical Graph Clustering using Node Pair Sampling
Social and Information Networks
2018-06-25 v2 Artificial Intelligence
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.
Cite
@article{arxiv.1806.01664,
title = {Hierarchical Graph Clustering using Node Pair Sampling},
author = {Thomas Bonald and Bertrand Charpentier and Alexis Galland and Alexandre Hollocou},
journal= {arXiv preprint arXiv:1806.01664},
year = {2018}
}