Contrastive deep graph clustering (CDGC) utilizes contrastive learning to group nodes into different clusters. Better augmentation techniques benefit the quality of the contrastive samples, thus being one of key factors to improve performance. However, the augmentation samples in existing methods are always predefined by human experiences, and agnostic from the downstream task clustering, thus leading to high human resource costs and poor performance. To this end, we propose an Attribute Graph Clustering method via Learnable Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for high-quality and suitable augmented samples for CDGC. Specifically, we design two learnable augmentors for attribute and structure information, respectively. Besides, two refinement matrices, including the high-confidence pseudo-label matrix and the cross-view sample similarity matrix, are generated to improve the reliability of the learned affinity matrix. During the training procedure, we notice that there exist differences between the optimization goals for training learnable augmentors and contrastive learning networks. In other words, we should both guarantee the consistency of the embeddings as well as the diversity of the augmented samples. Thus, an adversarial learning mechanism is designed in our method. Moreover, a two-stage training strategy is leveraged for the high-confidence refinement matrices. Extensive experimental results demonstrate the effectiveness of AGCLA on six benchmark datasets.

翻译：对比深度图聚类（CDGC）利用对比学习将节点划分为不同聚类。更优的增强技术能提升对比样本质量，因此成为改进性能的关键因素之一。然而现有方法中的增强样本通常由人类经验预定义且与下游聚类任务无关，导致人力成本高昂且性能不佳。为此，我们提出一种通过可学习增强的属性图聚类方法（\textbf{AGCLA}），该方法引入可学习增强器为CDGC生成高质量且适配的增强样本。具体而言，我们分别为属性信息和结构信息设计了两类可学习增强器。此外，我们生成高置信度伪标签矩阵和跨视图样本相似矩阵这两个精炼矩阵，以提升学习到的亲和矩阵的可靠性。在训练过程中，我们注意到训练可学习增强器与对比学习网络的优化目标存在差异，即需同时保证嵌入的一致性以及增强样本的多样性。为此，我们在方法中设计了对抗学习机制。同时，采用两阶段训练策略生成高置信度精炼矩阵。大量实验结果表明，AGCLA在六个基准数据集上均具有有效性。