上海科技大学知识管理系统

As an important sub-field in clustering analysis, deep graph clustering is receiving more and more attention from academia and industry. The goal of deep graph clustering is to learn effective embeddings for all nodes in the graph. Such node embeddings can effectively perform clustering task and thus be extended to various real-world application scenarios. However, existing graph clustering methods usually focus on technical-level improvements and ignore data-level information augmentation. In fact, data augmentation on graph data can effectively improve the receptive field and feature richness of model, and its effectiveness has been verified in many deep learning fields. Based on this motivation, we proposed a simple structure enhanced method for graph clustering, called SEGC. Such method only needs to construct a simple deep clustering network at the technical-level to achieve better performance by performing data augmentation at the data-level. Specifically, we leverage the underexplored potential of node activeness to perform edge-increasing and edge-decreasing operations on the original graph data, thereby generating different views to enhance the model's learning ability and receptive field. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of our method.