Despite its computational advantages offered by GPUs in GNN training, the limited GPU memory capacity struggles to accommodate large-scale graph data, making scalability a significant challenge for ...

School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China ...

However, existing methods lack the consideration of multi-scale information and tend to focus on extracting local graph features, especially overlooking the interdependencies between graph-level ...

Existing GNN models that tackle MIP problems are mostly constructed from mathematical formulation, which is computationally expensive when dealing with large-scale UC problems. In this paper, we ...