Deep Contrastive Graph Learning with Clustering-Oriented Guidance
Mulin Chen Bocheng Wang Xuelong Li
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.