A Distributed Training Architecture For Combinatorial Optimization

Recent advancements in graph neural networks (GNNs) have shown promise in addressing combinatorial optimization problems, but existing methods face challenges in accuracy and scalability, particularly with complex graphs. A new distributed GNN-based training framework has been proposed to tackle these issues by partitioning large graphs into smaller subgraphs, allowing for efficient local optimization. Reinforcement learning is then utilized to ensure that interactions between nodes are effectively learned. Extensive experiments conducted on real large-scale social network datasets, such as Facebook and YouTube, validate the framework's effectiveness, demonstrating that it outperforms state-of-the-art approaches in both solution quality and computational efficiency. This development not only addresses the limitations of current methods but also enhances the potential for GNNs in large-scale applications, marking a significant step forward in the field of combinatorial optimization.