Hierarchical Graph Clustering using Node Pair Sampling
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2018
- DOI:
- 10.48550/arXiv.1806.01664
- arXiv:
- arXiv:1806.01664
- Bibcode:
- 2018arXiv180601664B
- Keywords:
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- Computer Science - Social and Information Networks;
- Computer Science - Artificial Intelligence;
- I.5.2