Deep graph clustering network by community prediction-guided augmentation

Abstract

Deep graph clustering is a challenging field that help us analyze complex network data which involves dividing nodes into non-overlapping clusters. A key issue in this field is the reliance on complex data augmentation. Traditional data augmentation methods not only increase data processing time, but also require specialized knowledge to determine appropriate strategies. At the same time, traditional data augmentation may not fully utilize the topological properties of graph data, thus limiting the potential of graph clustering models. Based on these, we propose a novel unsupervised learning approach for automatically identifying and implementing effective data augmentation strategies, focusing on community-based augmentation. This approach dynamically analyzes graph data structures, reduces manual intervention, and improves the efficiency and accuracy of deep graph clustering. Our experimental results validate the efficiency of our method, which outperforms the models without considering information enhancement.