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Advanced Coarsening Schemes for Graph Partitioning

机译:图分区的高级粗化方案

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The graph partitioning problem is widely used and studied in many practical and theoretical applications.rnToday, multilevel strategies represent one of the most effective and efficient generic frameworks for solvingrnthis problem on large-scale graphs. Most of the attention in designing multilevel partitioning frameworksrnhas been on the refinement phase. In this work, we focus on the coarsening phase, which is responsiblernfor creating structures similar to the original but smaller graphs. We compare different matching- andrnAMG-based coarsening schemes, experiment with the algebraic distance between nodes, and demonstraterncomputational results on several classes of graphs that emphasize the running time and quality advantagesrnof different coarsening schemes.
机译:图划分问题在许多实际和理论应用中得到了广泛的应用和研究。当今,多级策略代表了解决大规模图上这一问题的最有效,最有效的通用框架之一。设计多层分区框架时,大多数注意力都集中在改进阶段。在这项工作中,我们集中在粗化阶段,该阶段负责创建类似于原始但较小的图的结构。我们比较了不同的基于匹配和AMG的粗化方案,对节点之间的代数距离进行了实验,并在几类图上展示了计算结果,这些图强调了不同粗化方案的运行时间和质量优势。

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