Employing graph neural networks (GNNs) for graph clustering has shown promising results in deep graph clustering. However, existing methods disregard the reciprocal relationship between representation learning and structure augmentation: the more homogeneous the graph, the more cohesive the node representations; the more cohesive the node representations, the more reliable the structure augmentation becomes. Moreover, the generalization ability of existing GNN-based models on the low homophily graph is relatively poor. To this end, we propose a graph clustering framework named Synergistic Deep Graph Clustering Network (SynC). SynC employs a Transform Input Graph Auto-Encoder (TIGAE) to obtain high-quality embeddings via mitigating the representations collapse issue of GAE for guiding structure augmentation. Then, we re-capture neighborhood representations on the refined graph to obtain clustering-friendly embeddings and conduct self-supervised clustering. Notably, these two stages share weights, resulting in synergistic boosting while significantly reducing the number of model parameters. Additionally, we introduce a structure fine-tuning strategy to improve the model's generalization on the low homophily graph. Extensive experiments on benchmark datasets demonstrate the superiority of SynC. The code is released at GitHub.

翻译：采用图神经网络进行图聚类在深度图聚类中展现出了良好的效果。然而现有方法忽视了表示学习与结构增强之间的互惠关系：图越同质，节点表示越具有内聚性；节点表示越具有内聚性，结构增强越可靠。此外，现有基于GNN的模型在低同质性图上的泛化能力相对较差。为此，我们提出了一种名为协同深度图聚类网络（SynC）的图聚类框架。SynC采用变换输入图自编码器（TIGAE），通过缓解图自编码器的表示坍缩问题获取高质量嵌入，以指导结构增强。随后，我们在优化后的图上重新捕获邻域表示，以获取利于聚类的嵌入并执行自监督聚类。值得注意的是，这两个阶段共享权重，从而实现协同增强的同时显著减少模型参数量。此外，我们引入结构微调策略以提升模型在低同质性图上的泛化能力。在基准数据集上的大量实验证明了SynC的优越性。代码已发布于GitHub。