Abstract: Mixup is an effective data augmentation technique in the field of computer vision.It is widely used for expanding the training distribution by interpolating input images and labels to generate new samples.However,in the context of graph node clustering tasks,designing robust interpolation methods poses challenges due to the irregularity and connectivity of graph data,as well as the unsupervised nature of the problem.To address these challenges,we propose a novel approach that leverages a dedicated encoder with non-shared parameters to extract embedding features from different views of graph.This allows us to effectively integrate both the node features and structural information.We then introduce Mixup into the clustering task by performing mixed interpolation on the embedding features along with their corresponding pseudo-labels.To ensure the reliability of these pseudo-labels,we apply a threshold to filter out high-confidence predictions,while incorporating an exponential moving average(EMA) mechanism for updating model parameters and considering the historical information during training.Furthermore,we incorporate a graph contrastive learning module to enhance feature consistency across different views,reducing information redundancy and improving the discriminative power of the model.Extensive experiments on six datasets demonstrate the effectiveness of the proposed method.

Key words: Data augmentation, Graph contrastive clustering, EMA, Mixup, Graph neural network