[2603.29148] Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification

Computer Science > Machine Learning arXiv:2603.29148 (cs) [Submitted on 31 Mar 2026] Title:Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification Authors:Guan Wang, Shuyin Xia, Lei Qian, Guoyin Wang, Yi Liu, Yi Wang, Wei Wang View a PDF of the paper titled Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification, by Guan Wang and 6 other authors View PDF HTML (experimental) Abstract:Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still faces the challenge of high computational overhead, especially when the number of convolutional layers in the graph is large. Currently, there are many advanced methods that use various sampling techniques or graph coarsening techniques to alleviate the inconvenience caused during training. However, among these methods, some ignore the multi-granularity information in the graph structure, and the time complexity of some coarsening methods is still relatively high. In response to these issues, based on our previous work, in this paper, we propose a new framework called Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification. Specifically, this method first uses a multi-granularity granular-ball graph coarsening algorithm to coarsen the original graph to obtain many subgraphs. The time...