  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.
