Graph contrastive learning is an important method for deep graph clustering. The existing methods first generate the graph views with stochastic augmentations and then train the network with a cross-view consistency principle. Although good performance has been achieved, we observe that the existing augmentation methods are usually random and rely on pre-defined augmentations, which is insufficient and lacks negotiation between the final clustering task. To solve the problem, we propose a novel Graph Contrastive Clustering method with the Learnable graph Data Augmentation (GCC-LDA), which is optimized completely by the neural networks. An adversarial learning mechanism is designed to keep cross-view consistency in the latent space while ensuring the diversity of augmented views. In our framework, a structure augmentor and an attribute augmentor are constructed for augmentation learning in both structure level and attribute level. To improve the reliability of the learned affinity matrix, clustering is introduced to the learning procedure and the learned affinity matrix is refined with both the high-confidence pseudo-label matrix and the cross-view sample similarity matrix. During the training procedure, to provide persistent optimization for the learned view, we design a two-stage training strategy to obtain more reliable clustering information. Extensive experimental results demonstrate the effectiveness of GCC-LDA on six benchmark datasets.
翻译:对比图形学习是深图群集的一个重要方法。 现有方法首先通过随机增强生成图形视图,然后用交叉视图一致性原则对网络进行培训。 虽然已经取得了良好的绩效,但我们发现,现有的增强方法通常是随机的,依赖预先定义的增强,这不够,而且最后组群任务之间缺乏谈判。 为了解决问题,我们建议了与可学习图形数据增强(GCC-LDA)的相异组合法,这是由神经网络完全优化的。 对抗性学习机制的设计是为了保持潜藏空间的交叉视图一致性,同时确保扩大观点的多样性。 在我们的框架里,为在结构级别和属性层面的增强学习建立了结构增强器和属性增强器。为了提高所学的接近性矩阵的可靠性,我们引入了学习程序,并且用高可信度伪标签矩阵和交叉视图样本相似性矩阵来完善所学的亲近性矩阵。 在培训过程中,为所学的观点提供持续的优化,我们设计了一个结构强化器和属性增强器,用于结构层次和属性水平的增强学习能力学习能力学习,我们设计了两阶段培训战略,以获得更可靠的海基化的海基数据组合结果。