Graph Convolutional Network (GCN) has exhibited remarkable potential in
improving graph-based clustering. To handle the general clustering scenario
without a prior graph, these models estimate an initial graph beforehand to
apply GCN. Throughout the literature, we have witnessed that 1) most models
focus on the initial graph while neglecting the original features. Therefore,
the discriminability of the learned representation may be corrupted by a
low-quality initial graph; 2) the training procedure lacks effective clustering
guidance, which may lead to the incorporation of clustering-irrelevant
information into the learned graph. To tackle these problems, the Deep
Contrastive Graph Learning (DCGL) model is proposed for general data
clustering. Specifically, we establish a pseudo-siamese network, which
incorporates auto-encoder with GCN to emphasize both the graph structure and
the original features. On this basis, feature-level contrastive learning is
introduced to enhance the discriminative capacity, and the relationship between
samples and centroids is employed as the clustering-oriented guidance.
Afterward, a two-branch graph learning mechanism is designed to extract the
local and global structural relationships, which are further embedded into a
unified graph under the cluster-level contrastive guidance. Experimental
results on several benchmark datasets demonstrate the superiority of DCGL
against state-of-the-art algorithms.

基于图卷积网络，提出了深度对比图学习模型（DCGL），用于一般数据聚类。该模型通过建立伪孪生网络，将自编码器与图卷积网络相结合，强调图结构和原始特征，并引入特征级对比学习来增强判别能力，利用样本与质心之间的关系作为聚类导向指引。然后，设计了双分支图学习机制来提取局部和全局结构关系，并在聚类级别对比指导下将其嵌入到统一的图中。在几个基准数据集上的实验结果表明，DCGL 相对于现有算法具有优越性。