A Potts model approach to unsupervised graph clustering with Graph Neural Networks
Abstract
Numerous approaches have been explored for graph clustering, including those which optimize a global criteria such as modularity. More recently, Graph Neural Networks (GNNs), which have produced state-of-the-art results in graph analysis tasks such as node classification and link prediction, have been applied for unsupervised graph clustering using these modularity-based metrics. Modularity, though robust for many practical applications, suffers from the resolution limit problem, in which optimization may fail to identify clusters smaller than a scale that is dependent on properties of the network. In this paper, we propose a new GNN framework which draws from the Potts model in physics to overcome this limitation. Experiments on a variety of real world datasets show that this model achieves state-of-the-art clustering results.
Cite
@article{arxiv.2308.09644,
title = {A Potts model approach to unsupervised graph clustering with Graph Neural Networks},
author = {Co Tran and Mo Badawy and Tyler McDonnell},
journal= {arXiv preprint arXiv:2308.09644},
year = {2023}
}