Abstract: 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.