Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming graph convolutional operation undermine the efficiency of these methods. To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function. As to the architecture, our network includes two main parts, i.e., pre-processing and network backbone. A simple low-pass denoising operation conducts neighbor information aggregation as an independent pre-processing, and only two multilayer perceptrons (MLPs) are included as the backbone. For data augmentation, instead of introducing complex operations over graphs, we construct two augmented views of the same vertex by designing parameter un-shared siamese encoders and corrupting the node embeddings directly. Finally, as to the objective function, to further improve the clustering performance, a novel cross-view structural consistency objective function is designed to enhance the discriminative capability of the learned network. Extensive experimental results on seven benchmark datasets validate our proposed algorithm's effectiveness and superiority. Significantly, our algorithm outperforms the recent contrastive deep clustering competitors with at least seven times speedup on average.
翻译:最近,在深图组群中,反向学习因其有希望的性能而引起了大量关注。然而,复杂的数据增强和耗时的图形变幻操作破坏了这些方法的效率。为了解决这一问题,我们提议了一个简单的对比图形组合算法(SCGC),从网络结构、数据增强和客观功能的角度改进现有方法。关于结构,我们的网络包括两个主要部分,即预处理和网络主干。一个简单的低空拆解操作将邻居信息汇总作为独立的预处理,而只有两个多层透视器(MLPs)作为主干线。对于数据增强,我们不是在图表上引入复杂的操作,而是通过设计未共享的参数和数据放大,直接腐蚀节流嵌入功能,来构建两个强化的相同垂直视图。最后,关于目标功能,为了进一步提高组合性,一个新的交叉视图结构一致性目标功能旨在增强所学网络的区别性能力。对于七个基准数据组群集的广泛实验结果,不是在图表上引入复杂的操作,而是通过设计参数来验证我们最新的平均数字组群集的优势和最强度。