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.

翻译：众多方法已被探索用于图聚类，包括那些优化模块度等全局准则的方法。近年来，图神经网络（GNNs）在节点分类和链接预测等图分析任务中取得了最先进成果，并已应用于基于模块度指标的的无监督图聚类。尽管模块度在众多实际应用中表现稳健，但其存在分辨率极限问题，即优化过程可能无法识别尺度小于网络属性依赖阈值的聚类。本文提出一种新的GNN框架，借鉴物理学中的Potts模型以克服这一局限性。在多种真实世界数据集上的实验表明，该模型取得了最先进的聚类结果。