Graph Partitioning

Graph partitioning is the first step in distributed graph computing tasks, aiming to achieve load balancing and minimize communication volume. By optimizing the partitioning of the graph, the efficiency and performance of large-scale graph data processing can be significantly improved, playing a crucial role in parallel computing environments. Graph partitioning techniques are widely applied in social network analysis, recommendation systems, and large-scale machine learning, ensuring the effective utilization of computing resources and high scalability of the system.