  Deep graph clustering, which aims to reveal the underlying graph structure
and divide the nodes into different groups, has attracted intensive attention
in recent years. However, we observe that, in the process of node encoding,
existing methods suffer from representation collapse which tends to map all
data into the same representation. Consequently, the discriminative capability
of the node representation is limited, leading to unsatisfied clustering
performance. To address this issue, we propose a novel self-supervised deep
graph clustering method termed Dual Correlation Reduction Network (DCRN) by
reducing information correlation in a dual manner. Specifically, in our method,
we first design a siamese network to encode samples. Then by forcing the
cross-view sample correlation matrix and cross-view feature correlation matrix
to approximate two identity matrices, respectively, we reduce the information
correlation in the dual-level, thus improving the discriminative capability of
the resulting features. Moreover, in order to alleviate representation collapse
caused by over-smoothing in GCN, we introduce a propagation regularization term
to enable the network to gain long-distance information with the shallow
network structure. Extensive experimental results on six benchmark datasets
demonstrate the effectiveness of the proposed DCRN against the existing
state-of-the-art methods.
